BR102023001968A2 - DYNAMIC ADJUSTMENT TREATMENT BUFFERS FOR PLANT TREATMENTS - Google Patents

Abstract

The present invention relates to an agricultural machine that travels through a field of plants, the agricultural machine accesses an image of a field including a plant and receives sensor signals from one or more sensors coupled to the agricultural machine. The agricultural machine applies image and sensor signals to a computer model to determine a spatial relationship between a processing mechanism of the agricultural machine and the plant. Determining the spatial relationship produces a measurement of uncertainty for an expected position of the treatment mechanism relative to an expected position of the plant. The agricultural machine adjusts a treatment buffer based on the uncertainty measurement. The agricultural machine treats the plant in the field by applying plant treatment to the plant based on the treatment buffer.

Description

DESCRIPTIVE REPORT REFERENCE FOR RELATED ORDERS

[001] This Application claims the benefit of Provisional Patent Application No. 63/316,356, filed March 3, 2022, the entirety of which is incorporated by reference.

TECHNICAL FIELD

[002] The matter described generally relates to agricultural technology and, in particular, to managing the application of a treatment compound to a plant in a field.

BACKGROUND

[003] Conventional agricultural machines for treating crops in a field are controlled by human operators. Currently, some operations can be computer-assisted, but human control is a predominant reality of agricultural machinery, and these agricultural machines with automated functionality encounter frequent problems that prevent normal operation. Attempts to automate agricultural machines encounter difficulties and frequent failures, often due to the variable reliability of the data, received from sensors on the automated agricultural machines, which the agricultural machines use to operate.

[004] Agricultural machines apply treatment compounds to plants in the fields. For example, an agricultural machine uses a nozzle to direct a fluid containing a treatment compound to a plant as the agricultural machine moves over or past the plant in the field. Wide distribution of treatment compounds across a field can be wasteful as only part of the treatment compounds reach the target plants. Meanwhile, accurately targeting plants for the application of treatment compounds is difficult, especially when an agricultural machine moves through a field.

SUMMARY

[005] In one embodiment of an agricultural machine method, as the agricultural machine moves through a field of plants, the agricultural machine dynamically adjusts a treatment buffer while performing a plant treatment in the field. The treatment buffer is a portion of a treatment area to which plant treatment is applied and includes a buffer region around the estimated position of a plant.

[006] The agricultural machine accesses an image of the field that includes a plurality of pixels. The plurality of pixels includes pixels representing a plant in the field. The agricultural machine also receives sensor signals from one or more sensors coupled to the agricultural machine. The sensor signals include representations of positioning information from a handling mechanism of the agricultural machine.

[007] The agricultural machine applies image and sensor signals to a computer model configured to determine a spatial relationship between the treatment mechanism and the plant. The agricultural machine can identify a set of pixels in the accessed image as the plant. The agricultural machine can determine, based on sensor signals, an expected position of the agricultural machine's handling mechanism. The agricultural machine determines an uncertainty measurement for the expected position of the treatment mechanism. The agricultural machine can determine, based on the set of pixels and the expected position, an expected position of the identified plant.

[008] The agricultural machine adjusts the treatment buffer based on the uncertainty measurement for the expected position of the treatment mechanism. The agricultural machine can generate machine instructions based on the expected position, pixel set and adjusted processing buffer. The machine instructions may include instructions for the agricultural machine to position the treatment mechanism of the agricultural machine to target the identified plant in the field and perform plant treatment for the identified plant using the positioned treatment mechanism and the adjusted treatment buffer. The agricultural machine treats the plant in the field by applying plant treatment to the plant according to the adjusted treatment buffer, which may be based on the generated machine instructions.

BRIEF DESCRIPTION OF THE DRAWINGS

[009] FIG. 1A illustrates an isometric view of an agricultural machine that performs agricultural actions of a treatment plan, according to an example embodiment.

[0010] FIG. 1B illustrates a top view of an agricultural machine, according to the example embodiment.

[0011] FIG. 1C illustrates an isometric view of an agricultural machine, according to a second example embodiment.

[0012] FIG. 2 illustrates a block diagram of a system environment for an agricultural machine in accordance with one or more example embodiments.

[0013] FIG. 3 illustrates a block diagram of a control system for an agricultural machine according to one or more example embodiments.

[0014] FIG. 4 illustrates a processing buffer in accordance with one or more example embodiments.

[0015] FIG. 5 illustrates a flowchart of a method of an agricultural machine for dynamically adjusting a treatment buffer in accordance with one or more example embodiments.

[0016] FIG. 6 illustrates a block diagram of components of an example machine for reading and executing instructions from a machine-readable medium in accordance with one or more example embodiments.

[0017] The figures represent various modalities for illustration purposes only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein.

DETAILED DESCRIPTION I. INTRODUCTION

[0018] As an agricultural machine moves through a field, the agricultural machine applies treatments to the plants. Plant treatments may include treating agents such as fertilizers, pesticides, or herbicides. Some farm machinery transmits plant treatment indiscriminately across the field as the farm machine moves across the field, but this treatment methodology is wasteful and in some cases may unintentionally apply plant treatment to plants that have not been targeted for that treatment. of plants. Furthermore, an agricultural machine typically carries a limited amount of plant treatment, and using more plant treatment than necessary limits the productivity of the agricultural machine.

[0019] High fidelity (e.g., precision and accuracy) of plant treatment to target plants conserves plant treatment and improves the functionality of the agricultural machine as it moves through the field. That is, increasing treatment precision and accuracy allows the agricultural machine to treat more plants using less plant treatment per plant than would occur if the agricultural machine were transmitting the plant treatment. However, high-fidelity targeting of plant treatment is difficult, especially in agricultural machinery with autonomous or semi-autonomous operating capabilities. As the farm machine hits plants as it moves through the field, the farm machine repeatedly changes location, often on varying terrain, under varying weather conditions, and with components of the farm machine mechanically changing position. All of this can affect the direction of the plant. Sensor data may be unreliable, potentially leading to errors. The latency from collecting data to triggering a processing mechanism (e.g. turning on a spray nozzle) increases the complexity of the task. Computing a high-fidelity plant care plan can be rigorous, adding even more complexity and delay to agricultural machine operation, circularly affecting precision and accuracy if not accounted for.

[0020] These and other factors can affect the precision and accuracy with which an agricultural machine can target a plant with a treatment mechanism to apply treatment to the plant. The uncertainty of the precision and accuracy of plant targeting varies from moment to moment as conditions change, the position of the agricultural machine relative to the plant changes, and new sensor data is factored into the agricultural machine. However, a change in uncertainty from one moment to the next can only affect the direction of the plant so much, as there are viable limits on the extent to which the spatial relationship between the agricultural machine and the plant can change from one moment to the next. As such, quantifying the uncertainty of this spatial relationship and using this uncertainty measurement as a factor to adjust the region of the field targeted by the agricultural machine to apply plant treatment to the plant can dynamically account for this time-varying uncertainty. This therefore provides greater certainty that the plant has been targeted precisely and accurately - as precisely and accurately as possible given the degree of uncertainty in the spatial relationship between the agricultural machine and the plant.

[0021] As described below, various embodiments of a technique for dynamically adjusting a treatment buffer for a plant treatment provide high-fidelity targeting of plant treatment, conserving the plant treatment and preventing misapplication of the plant treatment. When targeting a plant, the agricultural machine identifies the plant and directs plant treatment to both the plant and a buffer zone around the plant. The embodiments provided herein provide dynamic adjustment of these treatment buffers based on a measurement of uncertainty related to the spatial relationship between the agricultural machine and the plant.

II. FIELD MANAGEMENT AND TREATMENT PLANS Field Management

[0022] Agricultural managers (“managers”) are responsible for managing agricultural operations in one or more fields. Managers work to implement an agricultural objective within these fields and select from a variety of agricultural actions to implement that agricultural objective. Traditionally, managers are, for example, a farmer or agronomist working in the field, but they can also be other people and/or systems set up to manage agricultural operations in the field. For example, a manager can be an automated agricultural machine, a machine-learned computer model, etc. In some cases, a manager may be a combination of the managers described above. For example, a manager may include a farmer assisted by a machine-learned agronomy model and one or more automated agricultural machines, or it may be a farmer and an agronomist working together.

[0023] Managers implement one or more agricultural objectives for a field. An agricultural objective is typically a macro-level goal for a field. For example, macro-level agricultural objectives may include treating crops with growth promoters, neutralizing weeds with growth regulators, harvesting a crop with the best possible yield, or any other suitable agricultural objective. However, agricultural objectives can also be a micro-level goal for the field. For example, micro-level agricultural objectives may include treating a certain plant in the field, repairing or fixing a part of an agricultural machine, requesting feedback from a manager, etc. Of course, there are many possible agricultural objectives and combinations of agricultural objectives, and the examples described above are not intended to be limiting.

[0024] The objectives of agriculture are achieved by one or more agricultural machines performing a series of agricultural actions. Agricultural machines are described in more detail below. Agricultural actions are any operation implementable by an agricultural machine within the field that works towards an agricultural objective. Consider, for example, an agricultural goal of harvesting a crop with the best possible yield. This agricultural objective requires a series of agricultural actions, for example, planting the field, fertilizing the plants 104, watering the plants 104, weeding the field, harvesting the plants 104, evaluating the yield, etc. it can be an agricultural objective in itself. For example, planting the field may require its own set of agricultural actions, e.g. preparing the soil, digging the soil, planting a seed, etc.

[0025] In other words, managers implement a treatment plan in the field to achieve an agricultural objective. A treatment plan is a hierarchical set of macro- and/or micro-level objectives that fulfill the manager's agricultural objective. Within a treatment plan, each macro or micro objective may require a set of agricultural actions to be carried out, or each macro or micro objective may be an agricultural action in itself. So to expand, the treatment plan is a temporally sequenced set of agricultural actions to be applied to the field that the manager expects will meet the agricultural objective.

[0026] When executing a treatment plan in a field, the treatment plan itself and/or its constituent agricultural objectives and agricultural actions have various outcomes. An outcome is a representation of whether, or how well, an agricultural machine fulfilled the treatment plan, agricultural objective and/or agricultural action. An outcome can be a qualitative measurement, such as “accomplished” or “not accomplished,” or it can be a quantitative measurement, such as “40 pounds harvested” or “1.25 acres treated.” The results can also be positive or negative depending on the configuration of the agricultural machine or the implementation of the treatment plan. Furthermore, results can be measured by sensors on the agricultural machine, entered by managers or accessed from a data store or network.

[0027] Traditionally, managers have leveraged their experience, knowledge, and technical expertise when implementing agricultural actions into a treatment plan. In a first example, a manager might check weed pressure in various areas of the field to determine when a field is ready for weeding. In a second example, a manager might consult past implementations of a treatment plan to determine the best time to begin planting a field. Finally, in a third example, a manager may rely on established best practices to determine a specific set of agricultural actions to take in a treatment plan to achieve an agricultural objective.

[0028] Leveraging manager and historical knowledge to make decisions for a treatment plan affects the spatial and temporal characteristics of a treatment plan. For example, agricultural actions in a treatment plan have historically been applied to the entire field rather than small parts of a field. To illustrate, when a manager decides to plant a crop, he plants the entire field instead of just a corner of the field with the best planting conditions; or, when the manager decides to weed a field, he weeds the entire field instead of just a few rows. Likewise, each farm action in a treatment plan's farm action sequence is historically performed at approximately the same time. For example, when a manager decides to fertilize a field, he fertilizes the field at approximately the same time; or, when the manager decides to harvest the field, he does so at approximately the same time.

[0029] Notably, however, agricultural machinery has greatly advanced in its capabilities. For example, agricultural machinery continues to become more autonomous, includes an increasing number of sensors and measurement devices, employs greater amounts of processing power and connectivity, and implements various machine vision algorithms to enable managers to successfully implement a treatment plan.

[0030] Due to this increased capacity, managers are no longer limited to spatially and temporally monolithic implementations of agricultural actions in a treatment plan. Instead, managers can leverage the advanced capabilities of agricultural machinery to implement highly localized treatment plans determined by real-time measurements in the field. In other words, instead of a manager applying a “best guess” treatment plan to an entire field, they can implement informed, individualized treatment plans for each plant in the field.

III. AGRICULTURAL MACHINE Overview

[0031] An agricultural machine that implements agricultural actions of a treatment plan can have a variety of configurations, some of which are described in greater detail below.

[0032] Figure 1A is an isometric view of an agricultural machine 100 that performs agricultural actions of a treatment plan, according to an example embodiment, and Figure 1B is a top view of the agricultural machine 100 in Figure 1A. Figure 1C is an isometric view of another agricultural machine 100 that performs agricultural actions of a treatment plan, in accordance with an example embodiment.

[0033] The agricultural machine 100 includes a detection mechanism 110, a treatment mechanism 120, and a control system 130. The agricultural machine 100 may additionally include an assembly mechanism 140, a verification mechanism 150, a power source, digital memory, communication apparatus or any other suitable component that allows the agricultural machine 100 to implement agricultural actions in a treatment plan. Furthermore, the described components and functions of agricultural machine 100 are examples only and an agricultural machine 100 may have different or additional components and functions in addition to those described below.

[0034] Agricultural machine 100 is configured to perform agricultural actions on a field 160 and the implemented agricultural actions are part of a treatment plan. To illustrate, agricultural machine 100 implements an agricultural action that applies a treatment to one or more plants 104 and/or substrate 106 within a geographic area. Here, treatment crop actions are included in a treatment plan to regulate plant growth. As such, treatments are typically applied directly to a single plant 104, but may alternatively be applied directly to multiple plants 104, indirectly applied to one or more plants 104, applied to the environment 102 associated with the plant 104 (e.g., soil, atmosphere , or other suitable part of the plant environment adjacent to or connected by environmental factors such as wind), or otherwise applied to plants 104.

[0035] In a particular example, agricultural machine 100 is configured to implement an agricultural action that applies a treatment that necrotizes the entire plant 104 (e.g., weeding) or part of the plant 104 (e.g., pruning). In this case, the agricultural action may include dislodging the plant 104 from the supporting substrate 106, incinerating a portion of the plant 104 (e.g., with directed electromagnetic energy such as a laser), applying a working fluid treatment concentration (e.g., , fertilizer, hormone, water, etc.) for plant 104, or treat plant 104 in any other suitable manner.

[0036] In another example, agricultural machine 100 is configured to implement an agricultural action that applies a treatment to regulate plant growth. Regulation of plant growth may include promoting the growth of the plant, promoting the growth of a part of the plant, preventing (e.g., retarding) the growth of the plant 104 or part of the plant, or controlling the growth of the plant. Examples of regulating plant growth include applying growth hormone to plant 104, applying fertilizer to plant 104 or substrate 106, applying a disease treatment or insect treatment to plant 104, electrically stimulating the plant 104, watering the plant 104, pruning the plant 104, or otherwise treating the plant 104. Plant growth may further be regulated by pruning, necrosis, or other treatment of plants 104 adjacent to the plant 104.

Operating Environment 102

[0037] Agricultural machine 100 operates in an operating environment 102. Operating environment 102 is the environment 102 that surrounds agricultural machine 100 as it implements agricultural actions of a treatment plan. The operating environment 102 may also include the agricultural machine 100 itself and its corresponding components.

[0038] The operating environment 102 typically includes a field 160 and the agricultural machine 100 generally implements agricultural actions of the treatment plan in the field 160. A field 160 is a geographic area where the agricultural machine 100 implements a treatment plan. The field 160 may be an outdoor plant field, but it may also be an indoor location that houses plants, such as a greenhouse, a laboratory, a grow house, a set of containers, or any other suitable environment 102.

[0039] A field 160 may include any number of field parts. A field part is a subunit of a field 160. For example, a field part may be a part of the field 160 small enough to include a single plant 104, large enough to include many plants 104, or some other size. The agricultural machine 100 can perform different agricultural actions for different parts of the field. For example, agricultural machine 100 may apply a herbicide to some field portions in field 160, while applying a pesticide to another field portion. Furthermore, a field 160 and a field part are largely interchangeable in the context of the methods and systems described herein. That is, treatment plans and their corresponding agricultural actions can be applied to an entire field 160 or to a part of the field, depending on the circumstances at stake.

[0040] The operating environment 102 may also include plants 104. As such, the agricultural actions that the agricultural machine 100 implements as part of a treatment plan may be applied to the plants 104 in the field 160. The plants 104 may be crops, but they may also be weeds or any other suitable plant. Some examples of crops include cotton, lettuce, soybeans, rice, carrots, tomatoes, corn, broccoli, cabbage, potatoes, wheat or any other suitable cash crop. Weeds can be grasses, broadleaf weeds, thistles or any other suitable determinant weed.

[0041] More generally, plants 104 may include a stem that is disposed superiorly (e.g., above) the substrate 106 and a root system attached to the stem that is located below the plane of the substrate 106 (e.g., below ground). . The stem can support any branches, leaves and/or fruits. Plant 104 may have a single stem, leaf or fruit, multiple stems, leaves or fruits, or any number of stems, leaves or fruits. The root system may be a root system or fibrous root system, and the root system may support the position of the plant 104 and absorb nutrients and water from the substrate 106. In various examples, the plant 104 may be a vascular plant 104, not a vascular plant. 104, woody plant 104, herbaceous plant 104 or any suitable type of plant 104.

[0042] Plants 104 in a field 160 may be grown in one or more plant rows 104 (e.g., plant beds 104). The lines of plan 104 are normally parallel to each other, but they do not have to be. Each plan row 104 is generally spaced between 2 inches and 45 inches apart when measured in a perpendicular direction from an axis representing plan line 104. Plant rows 104 may have wider or narrower spacings or may have variable spacing between multiple lines (e.g., a 12-inch spacing between a first and second line, a 16-inch spacing between a second and third line, etc.).

[0043] Plants 104 within a field 160 may include the same type of crop (e.g., same genus, same species, etc.). For example, each field portion in a field 160 may include corn crops. However, the plants 104 within each field 160 may also include multiple crops (e.g., a first crop, a second crop, etc.). For example, some parts of the field may include lettuce crops, while other parts of the field include weeds, or, in another example, some parts of the field may include beans while other parts of the field include corn. Furthermore, a single part of the field may include different types of crops. For example, a single field part may include a soybean plant 104 and a weed.

[0044] The operating environment 102 may also include a substrate 106. As such, the agricultural actions that the agricultural machine 100 implements as part of a treatment plan may be applied to the substrate 106. The substrate 106 may be soil, but it may also be a sponge or any other suitable substrate 106. Substrate 106 may include plants 104 or may not include plants 104, depending on its location in field 160. For example, one part of substrate 106 may include a row of crops, while another part of substrate 106 between crop rows do not include plants 104.

III.A EXAMPLES OF MACHINE SETTINGS Detection mechanism(s)

[0045] The agricultural machine 100 may include a detection mechanism 110. The detection mechanism 110 identifies objects in the operating environment 102 of the agricultural machine 100. To do this, the detection mechanism 110 obtains information that describes the environment 102 (e.g. , sensor, or image data), and processes this information to identify pertinent objects (e.g., plants 104, substrate 106, people, etc.) in the operating environment 102. Identifying objects in the environment 102 further allows the agricultural machine 100 implement agricultural actions on field 160. For example, detection engine 110 may capture an image of field 160 and process the image with a plant identification model 104 to identify plants 104 in the captured image. The agricultural machine 100 then implements agricultural actions in the field 160 based on the plants 104 identified in the image.

[0046] Agricultural machine 100 may include any number or type of sensing mechanism 110 that may assist in determining and implementing agricultural actions. In some embodiments, the detection mechanism 110 includes one or more sensors. For example, the detection mechanism 110 may include a multispectral camera, a stereo camera, a CCD camera, a single-lens camera, a CMOS camera, hyperspectral imaging system, LIDAR (light detection and ranging system) system, a depth sensing system, dynamometer, IR camera, thermal camera, humidity sensor, light sensor, temperature sensor, ultrasonic sensor or any other suitable sensor. Additionally, the sensing mechanism 110 may include a sensor array (e.g., a camera array) configured to capture information about the environment 102 surrounding the agricultural machine 100. For example, the sensing mechanism 110 may include an array of cameras configured to capture an array of images representing the environment 102 around the agricultural machine 100. The detection mechanism 110 may also be a sensor that measures a state of the agricultural machine 100. For example, the detection mechanism 110 may be a speed sensor, a heat sensor, or some other sensor that can monitor the state of a component of the agricultural machine 100. Furthermore, the sensing mechanism 110 can also be a sensor that measures components during the implementation of an agricultural action. For example, the sensing mechanism 110 may be a flow rate monitor, a grain harvest sensor, a mechanical stress sensor, etc. Whatever the case, the sensing mechanism 110 detects information about the operating environment 102 (including the agricultural machine 100).

[0047] A detection mechanism 110 may be mounted at any point on the mounting mechanism 140. Depending on where the detection mechanism 110 is mounted in relation to the treatment mechanism 120, one or the other may pass through a geographic area in the field 160 before the other. For example, the detection mechanism 110 may be positioned in the mounting mechanism 140 so that it passes through a geographic location ahead of the treatment mechanism 120 as the agricultural machine 100 moves through the field 160. In other examples, the detection mechanism 110 is positioned for the mounting mechanism 140 so that the two traverse a geographic location at substantially the same time as the agricultural machine 100 moves through the field. Likewise, the detection mechanism 110 may be positioned in the mounting mechanism 140 so that the treatment mechanism 120 passes through a geographic location before the detection mechanism 110 as the agricultural machine 100 moves through the field 160. The detection mechanism 110 may be statically mounted to the mounting mechanism 140, or may be removable or dynamically coupled to the mounting mechanism 140. In other examples, the sensing mechanism 110 may be mounted to some other surface of the agricultural machine 100 or may be incorporated into another component of the agricultural machine 100.

Verification mechanism(s)

[0048] The agricultural machine 100 may include a verification mechanism 150. Generally, the verification mechanism 150 records a measurement of the operating environment 102 and the agricultural machine 100 may use the recorded measurement to verify or determine the extent of an implemented agricultural action (i.e. a result of agricultural action).

[0049] To illustrate, consider an example in which an agricultural machine 100 implements an agricultural action based on a measurement of the operating environment 102 by the detection mechanism 110. The verification mechanism 150 records a measurement of the same geographic area measured by the detection mechanism detection 110 and where the agricultural machine 100 implemented the determined agricultural action. The agricultural machine 100 then processes the recorded measurement to determine the result of the agricultural action. For example, the scanning engine 150 may record an image of the geographic region around a plant 104 identified by the detection engine 110 and treated by a processing engine 120. The agricultural machine 100 may apply a processing detection algorithm to the image. recorded to determine the result of the treatment applied to plant 104.

[0050] The information recorded by verification mechanism 150 may also be used to empirically determine the operating parameters of agricultural machine 100 that will achieve the desired effects of implemented agricultural actions (e.g., to calibrate agricultural machine 100, to modify plans treatment, etc.). For example, the agricultural machine 100 may apply a calibration detection algorithm to a measurement recorded by the agricultural machine 100. In this case, the agricultural machine 100 determines whether the actual effects of an implemented agricultural action are the same as its intended effects. If the effects of the implemented agricultural action are different from the intended effects, the agricultural machine 100 may perform a calibration process. The calibration process changes the operating parameters of the agricultural machine 100 so that the effects of future agricultural actions implemented are the same as their intended effects. To illustrate, consider the previous example in which the agricultural machine 100 recorded an image of a treated plant 104. There, the agricultural machine 100 may apply a calibration algorithm to the recorded image to determine whether the treatment is properly calibrated (e.g., in its objective location in the operational environment 102). If the agricultural machine 100 determines that the agricultural machine 100 is not calibrated (e.g., the applied treatment is in an incorrect location), the agricultural machine 100 may calibrate itself so that future treatments are in the correct location. Other example calibrations are also possible.

[0051] Verification engine 150 may have various configurations. For example, the verification mechanism 150 may be substantially similar to (e.g., be the same type of mechanism as) the detection mechanism 110 or may be different from the detection mechanism 110. In some cases, the detection mechanism 110 and the scan mechanism 150 may be one in the same (e.g., the same sensor). In an example configuration, the verification mechanism 150 is positioned distal to the detection mechanism 110 with respect to the direction of travel 115 and the treatment mechanism 120 is positioned between them. In this configuration, the verification mechanism 150 traverses a geographic location in the operating environment 102 after the processing mechanism 120 and the detection mechanism 110. However, the assembly mechanism 140 may retain the relative positions of the system components in any other configuration. proper. In some configurations, the checking mechanism 150 may be included in other components of the agricultural machine 100.

[0052] Agricultural machine 100 may include any number or type of verification mechanism 150. In some embodiments, verification mechanism 150 includes one or more sensors. For example, scanning engine 150 may include a multispectral camera, a stereo camera, a CCD camera, a single-lens camera, a CMOS camera, hyperspectral imaging system, LIDAR (light detection and ranging system) system, a depth sensing system, dynamometer, IR camera, thermal camera, humidity sensor, light sensor, temperature sensor or any other suitable sensor. Additionally, the scanning mechanism 150 may include a sensor array (e.g., a camera array) configured to capture information about the environment 102 surrounding the agricultural machine 100. For example, the scanning mechanism 150 may include an array of cameras configured to capture an array of images representing the operational environment 102.

Treatment mechanism(s)

[0053] Agricultural machine 100 may include a treatment mechanism 120. The treatment mechanism 120 may implement agricultural actions in the operating environment 102 of an agricultural machine 100. For example, an agricultural machine 100 may include a treatment mechanism 120 that applies a treatment (i.e., a plant treatment) to a plant 104, a substrate 106, or some other object in the operating environment 102. More generally, the agricultural machine 100 employs the treatment mechanism 120 to apply a treatment to a treatment area 122 and the treatment area 122 may include anything within the operating environment 102 (e.g., a plant 104 or the substrate 106). In other words, the treatment area 122 can be any part of the operating environment 102.

[0054] In some embodiments, the treatment mechanism 120 applies a treatment to a plant 104 in field 160. The treatment mechanism 120 may apply treatments to identified plants or unidentified plants. For example, the agricultural machine 100 may identify and treat a specific plant (e.g., plant 104) in the field 160. Alternatively, or additionally, the agricultural machine 100 may identify some other trigger that indicates a plant treatment and the treatment mechanism 120 can apply a plant treatment. Some examples of plant treatment mechanisms 120 include: one or more spray nozzles, one or more electromagnetic energy sources (e.g., a laser), one or more physical implements configured to manipulate plants, but other plant treatment mechanisms 104 120 are also possible.

[0055] Additionally, the effect of applying a plant treatment with a treatment mechanism 120 to a plant 104 may include any plant necrosis, plant growth stimulation, plant part necrosis or removal, plant part growth stimulation plant or any other suitable treatment effect. Additionally, treatment mechanism 120 may apply a treatment that dislodges a plant 104 from substrate 106, separates a plant 104 or part of a plant 104 (e.g., cutting), incinerates a plant 104 or part of a plant 104, stimulates electrically a plant 104 or part of a plant 104, fertilize or promote the growth (e.g., with a growth hormone) of a plant 104, water a plant 104, apply light or some other radiation to a plant 104, and/or inject one or more fluids in the substrate 106 adjacent to a plant 104 (e.g., within a threshold distance from the plant). Other plant treatments are also possible. When applying a plant treatment, the treatment mechanisms 120 can be configured to spray one or more of: a herbicide, a fungicide, insecticide, some other pesticide, or water.

[0056] In some embodiments, the treatment mechanism 120 applies a treatment to some part of the substrate 106 in field 160. The treatment mechanism 120 may apply treatments to identified areas of the substrate 106 or unidentified areas of the substrate 106. For example, the agricultural machine 100 may identify and treat an area of substrate 106 in the field 160. Alternatively, or additionally, the agricultural machine 100 may identify some other trigger that indicates a substrate treatment 106 and the treatment mechanism 120 may apply a treatment to the substrate 106. Some examples of treatment mechanisms 120 configured to apply treatments to substrate 106 include: one or more spray nozzles, one or more electromagnetic energy sources, one or more physical implements configured to manipulate substrate 106, but other substrate 106 mechanisms of treatment 120 are also possible.

[0057] Obviously, the agricultural machine 100 is not limited to treatment mechanisms 120 for plants 104 and substrates 106. The agricultural machine 100 may include treatment mechanisms 120 to apply various other treatments to objects in the field 160. Some other examples of mechanisms treatment system 120 may include: a flamethrower or a water hose.

[0058] Depending on the configuration, the agricultural machine 100 may include various numbers of treatment mechanisms 120 (e.g., 1, 2, 5, 20, 60, etc.). A treatment mechanism 120 may be fixed (e.g., statically coupled) to the mounting mechanism 140 or attached to the agricultural machine 100. Alternatively, or additionally, a treatment mechanism 120 may be movable (e.g., translatable, rotatable, etc. ) on the agricultural machine 100. In one configuration, the agricultural machine 100 includes a single treatment mechanism 120. In this case, the treatment mechanism 120 may be actuated to align the treatment mechanism 120 with a treatment area 122. In a second variation, the agricultural machine 100 includes a treatment mechanism assembly 120 comprising an array of treatment mechanisms 120. In this configuration, a treatment mechanism 120 may be a single treatment mechanism 120, a combination of treatment mechanisms 120, or the set of treatment mechanism 120. Thus, a single treatment mechanism 120, a combination of treatment mechanisms 120, or the entire set may be selected to apply a treatment to a treatment area 122. Likewise, the single, combination, or entire the assembly can be actuated to align with a treatment area as needed. In some configurations, the agricultural machine 100 may align a handling mechanism 120 with an identified object in the operating environment 102. That is, the agricultural machine 100 may identify an object in the operating environment 102 and actuate the handling mechanism 120 so that its treatment area aligns with the identified object.

[0059] A treatment mechanism 120 may be operable between a standby mode and a treatment mode. In the standby mode, the treatment mechanism 120 does not apply a treatment, and in the treatment mode, the treatment mechanism 120 is controlled by the control system 130 to apply the treatment. However, the treatment mechanism 120 may be operable in any other suitable number of operating modes.

Control system

[0060] The agricultural machine 100 includes a control system 130. The control system 130 controls the operation of the various components and systems in the agricultural machine 100. For example, the control system 130 may obtain information about the operating environment 102, process this information to identify an agricultural action to implement and implement the identified agricultural action with components of the agricultural machine system 100.

[0061] The control system 130 may receive information from the detection mechanism 110, the verification mechanism 150, the treatment mechanism 120 and/or any other component or system of the agricultural machine 100. For example, the control system 130 may receive measurements from the detection engine 110 or verification engine 150, or information related to the state of a treatment engine 120 or implemented agricultural actions from a verification engine 150. Other information is also possible.

[0062] Likewise, the control system 130 may provide input to the detection mechanism 110, the verification mechanism 150, and/or the processing mechanism 120. For example, the control system 130 may be configured as input and Agricultural machine control operating parameters 100 (e.g., speed, direction). Likewise, the control system 130 may be configured to input and control operating parameters of the detection mechanism 110 and/or verification mechanism 150. The operational parameters of the detection mechanism 110 and/or verification mechanism 150 may include timing. processing, location and/or angle of detection mechanism 110, image capture intervals, image capture settings, etc. Other inputs are also possible. Finally, the control system may be configured to generate machine inputs to the treatment engine 120. That is, translate an agricultural action from a treatment plan into machine instructions implementable by the treatment engine 120.

[0063] The control system 130 may be operated wholly or partially autonomously, by a user operating the agricultural machine 100, a user connected to the agricultural machine 100 by a network, or any combination of the foregoing. For example, the control system 130 may be operated by a farm manager seated in a cab of the farm machine 100, or the control system 130 may be operated by a farm manager connected to the control system 130 via a wireless network. . In another example, control system 130 may implement an array of control algorithms, machine vision algorithms, decision algorithms, etc. that allow it to operate autonomously or partially autonomously.

[0064] The control system 130 may be implemented by a computer or a distributed computer system. Computers can be connected in various network environments. For example, the control system 130 may be a series of computers implemented in the agricultural machine 100 and connected by a local network. In another example, the control system 130 may be a series of computers implemented on the agricultural machine 100, in the cloud, and/or on a client device and connected via a wireless area network.

[0065] Control system 130 may apply one or more computer models to determine and implement agricultural actions in field 160. For example, control system 130 may apply a plant identification module to images acquired by detection mechanism 110 to determine and implement agricultural actions. The control system 130 may be coupled to the agricultural machine 100 so that an operator (e.g., a driver) can interact with the control system 130. In other embodiments, the control system 130 is physically removed from the agricultural machine 100 and communicates with system components (e.g., detection engine 110, handling engine 120, etc.) wirelessly.

[0066] In some configurations, the agricultural machine 100 may additionally include a communications apparatus, which functions to communicate (e.g., send and/or receive) data between the control system 130 and a set of remote devices. The communication apparatus may be a Wi-Fi communication system, a cellular communication system, a short-range communication system (e.g., Bluetooth, NFC, etc.), or any other suitable communication system.

Other Machine Components

[0067] In various configurations, agricultural machine 100 may include any number of additional components.

[0068] For example, agricultural machine 100 may include a mounting mechanism 140. Mounting mechanism 140 provides a mounting point for components of agricultural machine 100. That is, mounting mechanism 140 may be a chassis or frame to which agricultural machine components the machine 100 may be attached, but may alternatively be any other suitable mounting mechanism 140. More generally, the mounting mechanism 140 statically retains and mechanically supports the positions of the sensing mechanism 110, the treatment mechanism 120 and the checking mechanism 150. In an exemplary configuration, the mounting mechanism 140 extends outward from an agricultural machine body 100 so that the mounting mechanism 140 is approximately perpendicular to the direction of travel 115. In some configurations, the mounting mechanism 140 may include an array of handling mechanisms 120 positioned laterally along the mounting mechanism 140. In some configurations, the agricultural machine 100 may not include an assembly mechanism 140, the mounting mechanism 140 may be alternatively positioned or the mounting mechanism 140 may be incorporated into any other component of the agricultural machine 100.

[0069] Agricultural machine 100 may include locomotion mechanisms. The locomotion mechanisms may include any number of wheels, continuous steps, articulated legs, or some other locomotion mechanism. For example, the agricultural machine 100 may include a first set and a second set of coaxial wheels, or a first set and a second set of continuous steps. In either example, the axis of rotation of the first and second set of wheels/tracks are approximately parallel. Furthermore, each assembly is disposed along opposite sides of the agricultural machine 100. Typically, the locomotion mechanisms are connected to a drive mechanism that causes the locomotion mechanisms to transfer the agricultural machine 100 through the operating environment 102. For For example, the agricultural machine 100 may include a drive train for running wheels or steps. In different configurations, the agricultural machine 100 may include any other suitable number or combination of locomotion mechanisms and drive mechanisms.

[0070] Agricultural machine 100 may also include one or more coupling mechanisms 142 (e.g., a hitch). The coupling mechanism 142 functions to statically or removably couple various components of the agricultural machine 100. For example, a coupling mechanism may attach a drive mechanism to a secondary component so that the secondary component is pulled behind the agricultural machine. 100. In another example, a coupling mechanism may couple one or more treatment mechanisms 120 to the agricultural machine 100.

[0071] The agricultural machine 100 may additionally include a power source, which functions to power system components, including the detection mechanism 110, control system 130, and treatment mechanism 120. The power source may be mounted on the mechanism mounting mechanism 140, may be removably coupled to the mounting mechanism 140 or may be incorporated into another component of the system (e.g., located in the drive mechanism). The power source may be a rechargeable power source (e.g., a set of rechargeable batteries), an energy harvesting power source (e.g., a solar system), a fuel-consuming power source (e.g., a fuel cell array or an internal combustion system), or any other suitable power source. In other configurations, the power source may be incorporated into any other component of the agricultural machine 100.

III. B SYSTEM ENVIRONMENT

[0072] Figure 2 is a block diagram of the system environment for agricultural machine 100, in accordance with one or more example embodiments. In this example, the control system 210 (e.g., control system 130 ) is connected to external systems 220 and an array of machine components 230 via a network 240 within the environment of the system 200 .

[0073] External systems 220 are any system that can generate data representing information useful for determining and implementing agricultural actions in a field. External systems 220 may include one or more sensors 222, one or more processing units 224, and one or more data stores 226. One or more sensors 222 may measure field 160, operating environment 102, agricultural machine 100, etc. . and generate data that represents these measurements. For example, the sensors 222 may include a rain sensor, a wind sensor, a heat sensor, a camera, etc. The processing units 2240 can process measured data to provide additional information that can assist in determining and implementing agricultural actions in the field. For example, a processing unit 224 may access an image of a field 160 and calculate a weed pressure from the image or may access historical weather information for a field 160 to generate a forecast for the field. Data stores 226 store historical information about the agricultural machine 100, the operating environment 102, the field 160, etc. which can be beneficial in determining and implementing agricultural actions in the field. For example, data storage 226 may store results of previously implemented treatment plans and agricultural actions for a field 160, a nearby field, and/or the region. The historical information may have been obtained from one or more agricultural machines (i.e., measuring the result of an agricultural action of a first agricultural machine with the sensors of a second agricultural machine). Additionally, data storage 226 may store results of specific agricultural actions in field 160, or results of agricultural actions performed in nearby fields with similar characteristics. Data storage 226 can also store historical weather, floods, field use, crops, etc. to the countryside and the surrounding area. Finally, data stores 226 may store any information measured by other components in the system 200 environment.

[0074] The machine component array 230 includes one or more components 232. The components 222 are elements of the agricultural machine 100 that can perform agricultural actions (e.g., a treatment mechanism 120). As illustrated, each component has one or more input controllers 234 and one or more sensors 236, but a component may include only sensors 236 or only input controllers 234. An input controller 234 controls the function of the component 232. For example, an input the controller 234 can receive machine commands via the network 240 and trigger the component 230 in response. A sensor 226 generates data representing measurements of the operating environment 102 and provides this data to other systems and components within the environment of the system 200. The measurements may be of a component 232, the agricultural machine 100, the operating environment 102, etc. For example, a sensor 226 may measure a configuration or state of component 222 (e.g., a setting, parameter, power load, etc.), measure conditions in the operating environment 102 (e.g., humidity, temperature, etc.), capturing information representing the operating environment 102 (e.g., images, depth information, distance information) and generating data representing the measurement(s).

[0075] The control system 230 receives information from external systems 220 and the machine component matrix 230 and implements a treatment plan in a field with an agricultural machine. In particular, control system 230 employs a treatment buffer module 212 to dynamically determine and implement the application of a plant treatment to a plant using a dynamic treatment buffer.

[0076] Network 250 connects nodes of system environment 200 to allow microcontrollers and devices to communicate with each other. In some embodiments, the components are connected within the network as a controller area network (CAN). In this case, within the network each element has an input and output connection, and the network 250 can translate information between the various elements. For example, network 250 receives input information from camera array 210 and component array 220, processes the information, and transmits the information to control system 230. Control system 230 generates an agricultural action based on the information and transmits instructions for implementing the agricultural action to the appropriate component(s) 222 of the component matrix 220.

[0077] Additionally, the environment of system 200 may be other types of network environments and include other networks or a combination of network environments with multiple networks. For example, the environment of system 200 may be a network such as the Internet, a LAN, a MAN, a WAN, a wired or wireless mobile network, a private network, a virtual private network, a direct communication line, and the like. .

Treatment Buffer Control System

[0078] Figure 3 illustrates a block diagram of the control system 130 of an agricultural machine according to one or more example embodiments. The control system 130 dynamically adjusts a treatment buffer of the agricultural machine 100. The control system 130 includes a pose module 305, a model module 310, a sensor module 315, a user interface (UI) module 320 , a treatment data store 325, a model data store 330, and a sensor data store 335. Alternative embodiments may include fewer, other, or additional components that provide the functionality described herein, without departing from the principles and techniques herein. settled down. Depending on the embodiment, the control system 130 may be the control system 210. and one or more of the treatment data store 325, the model data store 330 and the sensor data store 335 may be included in the data stores 330. data 226.

[0079] The pose module 305 coordinates the dynamic adjustment of a treatment buffer of the agricultural machine 100. Dynamically adjusting the treatment buffer is a change in the treatment buffer while the agricultural machine is operating in a field. A treatment buffer is a part of a treatment area to which plant treatment is applied. The treatment area includes an expected position of a plant, as well as the treatment buffer, which surrounds the expected position. In one embodiment, the treatment buffer is a circle of particular radius extending from the expected position of the plant. In another embodiment, the treatment buffer is a rectangle. The dynamic adjustment of the treatment buffer is affected by an uncertainty measurement of an expected position. An expected position is a point in space where a thing (e.g., a plant or a treatment mechanism) is estimated (e.g., by a computer model). The expected position may include a target area, which is a region, including the point in space, that the plant is estimated to occupy. An uncertainty measurement is a value that represents a degree of uncertainty in the estimate that produces an expected position, such as a variance produced by using a Kalman filter. Diffusion of a plant treatment is the application of the plant treatment to a maximum area over which the agricultural machine is capable of applying the plant treatment as it moves through the field.

[0080] In other words, as the uncertainty measurement of an expected position changes over time (e.g., the expected position of a treatment mechanism or the expected position of a plant), the pose module 305 adjusts the buffer of treatment for a treatment engine to apply treatment from plant to plant based on changes in the uncertainty measurement. The pose module 305 may adjust the processing buffer periodically, e.g., once per second.

[0081] If the expected position uncertainty measurement decreases from determining one fit to the next (e.g., after the pose module 305 factors for a new image and/or new sensor signals), the pose module 305 generates a smaller treatment buffer, for example, a treatment buffer covering a smaller area of the field. Conversely, if the expected position uncertainty measurement increases from determining one fit to the next (e.g., after the pose module 305 factors for a new image and/or new sensor signals), the pose module 305 generates a larger treatment buffer, for example, a treatment buffer covering a larger area of the field. Such techniques are described below, for example, with reference to Figure 5.

[0082] In one embodiment, the pose module 305 monitors uncertainty measurements. If an uncertainty measurement exceeds an uncertainty measurement threshold value, the pose module 305 adjusts the treatment buffer so that the agricultural machine transmits the plant treatment. In one embodiment, the uncertainty measurement threshold value includes an uncertainty measurement amount that a set of consecutive uncertainty measurements exceeds in order for the pose module 305 to convey the plant treatment. In an alternative embodiment, the pose module 305 increases the processing buffer by no more than a maximum amount per adjustment, regardless of an amount of an uncertainty measurement or a number of consecutive uncertainty measurements that exceed the measurement threshold value. of uncertainty.

[0083] Model module 310 manages the maintenance and use of one or more computer models in model data store 330. Model module 310 receives a request from pose module 305 to apply data (e.g., a image and one or more sensor signals) to one or more computer models managed by the model module 310. The model module 310 applies the data to one or more computer models and sends the model output to the pose module 305 In one embodiment, the model module 310 processes the model output before sending the model output to the pose module 305. For example, the model module 310 may format the model output into a specific data format. In one embodiment, the one or more computer models in the model data store 330 are machine learned and the model module 310 trains and/or retrains the one or more machine learned computer models.

[0084] Sensor module 315 manages sensor signals received from one or more sensors on the agricultural machine 100 and/or remote from the agricultural machine (e.g., from a server providing weather data). The sensor module 315 stores the sensor signals in the sensor data store 335 and retrieves the sensor signals from the sensor data store 335 upon receipt of a request for a sensor signal from the pose module 305 (e.g., a request for a sensor signal from a specific sensor at a specific time). In one embodiment, the one or more sensors are one or more image sensors (e.g., cameras) on the agricultural machine 100.

[0085] UI module 320 generates one or more user interfaces for presenting processing buffer information. The agricultural machine 100 may send one or more user interfaces for presentation to a monitor on the agricultural machine 100 or to a remote device. The UI module 320 may use historical processing data to generate user interfaces. For example, the UI module 320 retrieves treatment data from the treatment data store 325 and populates a graph of the treatment buffer size over time using the retrieved treatment data. As a particular example, the UI module 320 may generate a user interface that represents treatment data as a histogram where treatment buffers are grouped by size and/or uncertainty measurements are grouped by value, e.g. three containers corresponding to “low”, “medium” and “high” confidence. A “high” confidence interval may include uncertainty measurements less than or equal to a first confidence threshold value. An “average” confidence interval may include measurements of uncertainty between the first confidence limit value and a second confidence limit value. A “low” confidence bucket can include uncertainty measurements greater than the second confidence threshold value.

[0086] A generated user interface may include a representation of the processing buffer. For example, the generated user interface may include an electronic map representing the field or a portion of the field and an overlay indicating a subportion to which the plant treatment has been applied at a particular time, a current subportion to which the plant treatment is applied. in the application process and/or a future subportion to which the agricultural machine plans to apply the plant treatment (e.g., as indicated by the generated machine instructions). As another example, the treatment buffer representation may include an image of the field (e.g., an image including the plant) and an overlay indicating a region of the field represented in the image to which the plant treatment was applied at a particular time. the plant treatment is in the process of being applied and/or the agricultural machine plans to apply the plant treatment (e.g., as indicated by the generated machine instructions). As another example, the treatment buffer representation may include a graphical element indicating a confidence level captured by the treatment buffer.

[0087] The control system 130 may receive user input via a generated user interface indicating a change in the level of trust captured by the processing mechanism. For example, user input may indicate that the confidence level for the expected position of the treatment mechanism to which the treatment buffer is to apply plant treatment should change from 95% confidence to 90% confidence. The control system 130 may update accordingly so that the treatment buffer tracks a 90% confidence interval. In one embodiment, the control system 130 may receive a manual override via user input via a generated user interface. Manual override may set the processing buffer to a specific size and the control system 130 responsively adjusts the processing buffer to the specific size. Upon receiving subsequent user input to terminate the manual override, control system 130 terminates the manual override. Depending on the embodiment, control system 130 may interrupt treatment buffer adjustments generated during a manual override. Alternatively, control system 130 may generate a treatment buffer adjustment but refrain from applying the adjustment to the treatment buffer when a manual override is set.

[0088] The treatment data store 325 is a data store that records historical treatment data for the agricultural machine 100. The treatment data store 325 records treatment buffers over time. For example, the processing data store 325 may include time series data records that store the uncertainty measurement and/or the processing buffer size for one or more processing engines 120 at one or more times. Depending on the embodiment, the treatment data store 325 may additionally record, for one or more times, whether the treatment buffer was manually overwritten at that time and, if so, what the treatment buffer manual override setting was at that time. . In one embodiment, the treatment data store 325 stores machine instructions generated by the control system 130, for example, to position the treatment mechanism and/or perform plant treatment. In one embodiment, the processing data store 325 stores one or more user interfaces for presenting processing buffer information, e.g., user interfaces generated by the UI module 320. In one embodiment, the control system 130 reports one or more registered handling buffers and/or user interfaces to a remote system.

[0089] Model data store 330 is a data store that maintains one or more computer models. For example, data store model 330 may include a plant identification model, an agricultural machine location estimation model, and a plant location estimation model. One or more models in model data store 330 may include a Kalman filter. In one embodiment, the model data store 330 stores a computer model that includes a plurality of other computer models. For example, data store model 330 may include a plant targeting model that includes a plant identification model, an agricultural machine location estimation model, and a plant location estimation model.

[0090] Sensor data store 335 stores sensor signals received from one or more sensors (e.g., detection mechanism 110). For example, the sensor data store 335 may store one or more images captured by image sensors in the agricultural machine 100. When the agricultural machine (e.g., control system 130) accesses an image, the agricultural machine requests the image from the sensor data store 335 and the sensor data store 335 sends the accessed image to the control system 130.

[0091] Figure 4 illustrates a treatment buffer according to one or more example embodiments. Plant 104 is identified by control system 130 as occupying the expected position of plant 405. For application of a plant treatment, agricultural machine 100 generates machine instructions to apply the plant treatment to a treatment area 420 including both the expected position of the plant 405 and a treatment buffer 410. In one embodiment, the treatment buffer 410 has an insignificant radius and the treatment area 420 is simply the expected position of the plant 405. However, typically, the treatment area 420 includes the expected position of the plant 405 and a treatment buffer 410 around the expected position of the plant 405. For example, the expected position of the plant 405 may be a point in space and the treatment area 420 is defined by the area of a circle including a buffer treatment 410 of particular radius extending from the point. In alternative embodiments, alternative shapes may be employed by the agricultural machine 100. For example, the agricultural machine 100 may determine a treatment area 420 that is rectangular, another geometric shape, or an irregular shape.

[0092] The size of the treatment buffer 410, e.g., a radius of the treatment buffer 310 that extends from the expected position of the plant 405, may be adjusted based on the uncertainty measurement for the expected position of the treatment mechanism 120 and /or in measuring uncertainty for the expected position of the plant. The expected position of plant 405 is a probabilistic estimate, and the probability that a given area around the expected position of plant 405 actually includes plant 104 increases as the given area grows. Measuring uncertainty affects the probability that a given area of a particular size includes plant 104 (e.g., measuring uncertainty affects the confidence interval of the expected position of plant 405). For example, the 99.7% confidence band 415 indicates a given area corresponding to a 99.7% probability of the given area, including plant 104.

[0093] Treatment buffer 410 may be adjusted such that the given area captured by treatment buffer 410 maintains approximately (e.g., within 1% probability) a particular confidence level of containing plant 104. As such, If the uncertainty measurement increases, the confidence interval corresponding to the particular confidence level will expand, and therefore the agricultural machine 100 adjusts the treatment buffer to expand accordingly. Likewise, if the uncertainty measurement decreases, the confidence interval corresponding to the particular confidence level will decrease, and therefore, the agricultural machine 100 adjusts the treatment buffer to retract toward the expected position of the plant 405 accordingly.

[0094] In one embodiment, the treatment buffer 410 may be defined (e.g., by a farm manager), to include a minimum and/or maximum radius. In one embodiment, the treatment buffer 410 may be replaced manually (e.g., by a farm manager) as described above; This manual override may set the treatment buffer 410 to a specific radius length or a specific confidence level. For example, the agricultural machine 100 may receive instructions from an agricultural manager to transmit plant treatment, and thus the agricultural machine 100 expands the treatment area 420 to a maximum area supported by the corresponding treatment mechanism 120.

[0095] In one embodiment, treatment buffer 410 is selected by control system 130 from a set of treatment buffer configurations 410 (e.g., treatment buffer sizes) by matching an uncertainty measurement to a bucket, where each configuration corresponds to a bucket of a set of buckets that group uncertainty measurements into different ranges. Each bucket is associated with a different processing buffer configuration 410. The control system 130 combines the uncertainty measurement with a bucket with a respective range including the value of the uncertainty measurement and uses the corresponding treatment buffer configuration 410 of the corresponding bucket.

[0096] Figure 5 illustrates a flowchart of a method 500 of an agricultural machine 100 to dynamically adjust a treatment buffer 410, according to an example embodiment.

[0097] Agricultural machine 100 carries out plant treatment in a field. For example, agricultural machinery can use spray nozzles to apply herbicide to weeds in the field. The agricultural machine 100 includes one or more detection mechanisms 110 that detect plants in the field. In one embodiment, the sensing mechanisms 110 include an image sensor (e.g., a camera) and the agricultural machine 100 stores the captured images in memory (e.g., the sensor data store 335).

[0098] Agricultural machine 100 accesses 505 an image of the field. The field image includes a plurality of pixels and the plurality of pixels includes pixels representing a plant in the field. The image may be associated with a timestamp indicating a time at which the image was captured, which the agricultural machine 100 uses, for example, to associate the image with sensor signals received at approximately the same time (e.g., within of a tenth of a second). In alternative embodiments, the agricultural machine 100 may not include an image sensor. In such embodiments, the agricultural machine 100 may employ alternative sensors to detect the plant in the field and employ techniques described herein without departing from the principles presented herein.

[0099] The agricultural machine 100 receives 510 sensor signals from one or more sensors coupled to the agricultural machine. The sensor signals include representations of positioning information from a handling mechanism of the agricultural machine. Sensors may include, for example, ultrasonic sensors, image sensors, inertial measurement units (IMUs), GPS receivers, and so on. As a particular example, the sensor signals may include a representation of an elevation of the mounting mechanism above the field surface, e.g., the height of a boom. Agricultural machine 100 may store sensor signals in memory (e.g., sensor data store 335). The sensor signals may be associated with timestamps indicating the times at which the sensor signals were received, which the agricultural machine 100 may use, for example, to associate the sensor signals with other sensor signals received at approximately the same time. same time (e.g. within a tenth of a second).

[00100] In one embodiment, the sensor signals may include representations of external condition information. External condition information includes information that can affect the accuracy and precision of estimating an expected position. For example, external condition information may include data indicating environmental conditions, such as weather information (e.g., measurements of sunlight, rain, wind, and so on), soil information (e.g., a field topography), about plants (e.g., a plant height) and agricultural machine information (e.g., a speed of the agricultural machine, a shaking amplitude of an assembly mechanism 140, and so on).

[00101] Agricultural machine 100 applies 515 image and sensor signals to a computer model configured to determine a spatial relationship between the treatment mechanism and the plant that includes an uncertainty measurement for an expected position of the treatment mechanism in relative to an expected position of the plant. Depending on the embodiment, the computer model may include multiple computer models, one or more of which may be machine learned. For example, the computer model may include a plant identification model, an agricultural machine location estimation model 100, a plant location estimation model, and so on. In one embodiment, the computer model includes a Kalman filter.

[00102] In one embodiment, using the computer model, the agricultural machine 100 identifies a set of pixels from the accessed image as the plant (e.g., the agricultural machine 100 performs segmentation on the image and classifies a segment as the plant 104) . Using the computer model, the agricultural machine 100 determines, based on sensor signals, an expected position of the treatment mechanism 120 of the agricultural machine 100. The expected position of the treatment mechanism 120 may include a point in the three-dimensional coordinate space in relation to the field. Using the computer model, the agricultural machine determines an uncertainty measurement for the expected position of the treatment mechanism. For example, the uncertainty measurement may be a value that is a function of the size of a confidence interval for a given confidence level of the expected position of the treatment mechanism. As a particular example, the uncertainty measurement may be a radius, in inches, of the confidence interval as it extends from the center point of the expected position of the treatment mechanism.

[00103] In one embodiment, using the computer model, the agricultural machine 100 determines, based on the set of pixels and the expected position of the treatment mechanism 120, an expected position of the identified plant 104. By determining an expected position of the mechanism of treatment mechanism 120 and an expected position of the identified plant, the agricultural machine 100 may determine a spatial relationship between the treatment mechanism and the plant - for example, a vector from one to the other, or a position of each on an electronic map from Camp. Depending on the embodiment, the estimated position of the treatment mechanism 120 and the estimated position of the plant may be in terms of a global reference frame (e.g., latitude and longitude or coordinates within a map of the field), a reference frame of the treatment mechanism 120, or of a plant reference frame.

[00104] The agricultural machine 100 adjusts 520 the treatment buffer of the treatment mechanism 120 based on the uncertainty measurement. For example, the agricultural machine 100 may increase the treatment buffer so that the treatment area increases, or the agricultural machine 100 may shrink the treatment buffer so that the treatment area decreases in size. In one embodiment, the agricultural machine 100 includes an adjustment limit on the extent to which the treatment buffer can change size from one treatment buffer adjustment to the next. This adjustment limit may be based on the extent to which the spatial relationship between the treatment mechanism 120 and the plant 104 may have changed during a time window from one adjustment to the next adjustment. In one embodiment, the computer model includes a Kalman filter, the uncertainty measurement includes a variation of a state estimate of the Kalman filter (e.g., where the state estimate is an expected position of the treatment mechanism), and the Treatment buffer size is a function of (e.g., directly correlated with) a number of (e.g., two) standard deviations of the variance.

[00105] In one embodiment, the agricultural machine 100 varies a plant treatment application rate through the treatment mechanism based on the measurement of uncertainty. For lower uncertainty measurements, the agricultural machine applies the plant treatment at a higher rate, and for lower uncertainty measurements, the agricultural machine applies the plant treatment at a lower rate.

[00106] In one embodiment, the agricultural machine 100 may generate 525, based on the expected position of the treatment mechanism, the expected position of the identified plant and the adjusted treatment buffer, machine instructions for the agricultural machine to position the treatment mechanism of the agricultural machine machine to reach the identified plant in the field and carry out plant treatment for the identified plant using the positioned treatment mechanism and the adjusted treatment buffer. The agricultural machine 100 positioning the treatment mechanism may include physically calibrating the treatment mechanism, such as changing its position on the agricultural machine 100, changing the direction the treatment mechanism faces relative to the field, increasing or decreasing an opening of the treatment mechanism. treatment, or so on. Alternatively or additionally, the agricultural machine 100 positioning the treatment mechanism may include defining a series of treatment mechanisms to activate (e.g., on or off) and/or a time window for which the mechanism(s) will be activated. treatment mechanism is(s) activated to apply the treatment plant to the treatment area (for example, machine instructions may include a start time at which a treatment mechanism is activated and an end time at which the processing mechanism is disabled, for one or more processing mechanisms). For example, if the agricultural machine includes 100 spray nozzles, the agricultural machine may set five specific spray nozzles to be activated for a specific five-second time window in order to apply plant treatment to plant 104. In one embodiment, the treatment mechanism 120 is a spray nozzle, the treatment buffer is a spray buffer, and the plant treatment is a fluid that the treatment mechanism sprays. Adjusting the treatment buffer in this embodiment may include adjusting a duration for which the spray nozzle sprays fluid, adjusting a number of spray nozzles activated in the treatment mechanism, adjusting a spray nozzle opening, adjusting a direction and/or position of the spray nozzle and/or adjust a pulse width modulation duty cycle of the spray nozzle.

[00107] The agricultural machine 100 treats 530 the plant in the field by applying the plant treatment to the plant according to the adjusted treatment buffer. This may be based on the generated machine instructions, if the machine instructions are generated 525 as described.

Examples of Uncertainty Measurement Factors

[00108] Depending on the embodiment, the factors considered by the pose module 305 in determining the uncertainty measurement may be one or more of a variety of factors, including, but not limited to, image data and sensor data described above. These factors may include one or more of the following, depending on the modality.

[00109] Terrain variation assumption: The pose module 305 can take into account the roughness of the terrain of the field in which the agricultural machine operates. Terrain roughness, or terrain variation, is a representation of how uneven the terrain is. This can be based on various sensor data, such as height sensor data, accelerometer data, and so on. Terrain roughness can contribute to measurement uncertainty due to the difference between where the height is measured and where the camera and nozzle are located. Terrain roughness varies with region, soil type, and agricultural practices (irrigated vs. uncultivated vs. no-till, and so on).

[00110] The pose module 305 may also take into account terrain variation error based on the height of plants, creating a projection error in image data pixels representing the plant. The pose module 305 can measure the height of the plant and use the height to scale the error.

[00111] The pose module 305 can also factor for terrain variation error based on spray factors. Variation in plant height, at least partially due to variation in terrain height, can create errors, which the pose module 305 accounts for.

[00112] Boom height measurement error: Sensor data representing boom height has an inherent error. The pose module 305 may measure the noise of the sensor data to scale the error estimate and filter the sensor data with at least a threshold amount of noise.

[00113] IMU measurement error: The pose module 305 can take IMU error into account and can take into account the amount of overall movement, where greater detected movement signals greater uncertainty and less detected movement signals less uncertainty.

[00114] GPS Error: The pose module 305 can take into account heading error in the GPS data, as well as whether the GPS system is using real-time kinematics (RTK) to increase accuracy. The pose module 305 can compare variations in the speed of the agricultural machine, as determined by GPS tracks, with known information from the agricultural machine, such as the mass of the agricultural machine, to check for errors in the speed determined by the GPS.

[00115] Vehicle acceleration error: The pose module 305 may take into account the error in calculating the acceleration by comparing sensor data from the IMU, GPS and/or other sensors to estimate the error due to acceleration, which the pose module pose 305 can then factor.

[00116] Detection error: The pose module 305 may factor for misclassification of pixels in image data, such as pixels at the edge of crops and weeds. The pose module 305 may use the proximity of the misclassified pixel to the pixels representing the plant to scale the error generated by the misclassification, which may be factored out by the pose module 305.

[00117] Intrinsic camera calibration error: pose module 305 may take into account distortion of image data, which may cause error. The error from distorted image data is magnified at the edges and corners of an image. The pose module 305 may factor in the position of the plant within the image data to scale the error provided by the deformation of the image data.

[00118] Extrinsic camera calibration error: The pose module 305 can take into account image-to-image error and camera-to-camera calibration error. The pose module 305 can take into account differences in calibration between different cameras and differences in image data produced by the different cameras.

[00119] Nozzle calibration error: The pose module 305 may take into account the error in the nozzle calibration. The pose module 305 may use variation in nozzle calibration variables over time, or nozzle type functions, to scale the error.

[00120] Quantization: The pose module 305 can take into account the quantization done by the agricultural machine, such as map resolution, spraying time and image downsampling. Each of these can create an artificial buffer and thus reduce the need for additional buffering. The time-related quantization can be factored by the pose modulus 305 as a function of the speed of the agricultural machine.

[00121] Spray command delay variation: The pose module 305 may take into account a time delay between sending and receiving a spray command, which may be variable and therefore generate error. This variation can be measured and factored by the pose module 305, which can scale the error as a fraction of the traffic on a bus or agricultural machine transmission rate.

[00122] Processing time variation: The pose module 305 can take into account the processing time of a spray command, which can be variable and can be affected by factors such as processor speed and loading time on various components of the agricultural machine. The pose module 305 may further factor in the time when a message is received in an event loop.

[00123] Valve transition variation: The pose module 305 can take into account the time involved in opening or closing a valve, which can be variable and sometimes measured directly. This variation can be factored by the pose module 305 into the error estimation.

[00124] Pressure variation: The pose module 305 can take into account the pressure in a valve, which can affect the speed of drops leaving the nozzle connected to the valve. Measuring this variation directly can be used to estimate the error, and indirectly estimating the variation using the number of nozzles at a given time can be employed by the pose module 305 as well.

[00125] Droplet size variation: The pose module 305 can factor for the distribution of droplet sizes produced by each nozzle. The distribution tightness of each nozzle may affect the error. Factors that influence droplet size include material chemistry (e.g., surfactants), temperature, humidity, overall pressure, nozzle type, and so on.

[00126] Wind: The pose module 305 can factor for wind conditions when generating the uncertainty measurement, where higher wind speeds generate greater error. The scale of the error can be modeled by the pose module 305 based on factors such as droplet size, boom height, machine speed, system pressure, and environmental conditions.

[00127] Boom movement and bending: The pose module 305 can take into account whiplash and other movements generated by the boom joints, as well as the bending of individual boom segments. This motion may be modeled by the pose module 305 and an estimate of the motion may be scaled by the pose module 305 based on sensor data and other data from the agricultural machine, such as speed, direction, IMU measurements, and so on.

[00128] Flow development variation: The pose module 305 can take into account the time variation of the nozzle. Nozzle timing can be affected by flow development within the valve body and nozzle cavity. The impact may be based on the valve type and/or nozzle type, and the pose module 305 may directly measure the nozzle timing variation to account for the respective error created by such variation.

[00129] Nozzle wear: The pose module 305 may take into account the variation in the nozzle flow rate, which produces error. Nozzle flow rates may vary based on manufacturing tolerances and/or wear over time. Changes in flow rates affect droplet velocity and therefore spray time. This variation can be measured or estimated based on nozzle usage by pose module 305. For example, pose module 305 can track nozzle usage per nozzle and use this to estimate flow rate.

[00130] Various other sources of error may be used by pose module 305 to inform uncertainty measurement without departing from the principles established herein.

IV. COMPUTER COMPONENTS

[00131] Figure 6 is a block diagram illustrating components of an example machine for reading and executing instructions from a machine-readable medium. Specifically, Figure 6 shows a schematic representation of the control system 130 in the form of an example of a computer system 600. The computer system 600 may be used to execute instructions 624 (e.g., program code or software) to do with that the machine performs any one or more of the methodologies (or processes) described here. In alternative embodiments, the machine operates as a stand-alone device or a connected (e.g., networked) device that connects to other machines. In a network deployment, the machine can operate in the capacity of a server machine or a client machine in a server-to-client network environment or as a peer-to-peer machine in a peer-to-peer (or distributed) network environment.

[00132] The machine can be a server computer, a client computer, a personal computer (PC), a tablet PC, a set-top box (STB | set-top box), a smartphone, an internet of things device (IoT | internet of things), a network router, switch or bridge, or any machine capable of executing 624 instructions (sequential or otherwise) that specify actions to be performed by that machine. Furthermore, while only a single machine is illustrated, the term “machine” should also be considered to include any collection of machines that individually or together execute instructions 624 to execute any one or more of the methodologies discussed herein.

[00133] Example computer system 600 includes one or more processing units (generally processor 602). Processor 602 is, for example, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a controller, a state machine , one or more application specific integrated circuits (ASICs), one or more radio frequency integrated circuits (RFICs), or any combination thereof. The computer system 600 also includes a main memory 604. The computer system may include a storage unit 616. The processor 602, the memory 604, and the storage unit 616 communicate via a bus 608.

[00134] Additionally, the computer system 600 may include a static memory 606, a graphical display 610 (e.g., for controlling a plasma display panel (PDP), a liquid crystal display (LCD) liquid crystal display) or a projector). Computer system 600 may also include alphanumeric input device 612 (e.g., a keyboard), a cursor control device 614 (e.g., a mouse, a trackball, a joystick, a motion sensor, or other pointing instrument) , a signal generating device 618 (e.g., a speaker), and a network interface device 620, which are also configured to communicate via bus 608.

[00135] Storage unit 616 includes a machine-readable medium 622 on which instructions 624 (e.g., software) incorporating any one or more of the methodologies or functions described herein are stored. For example, instructions 624 may include the functionalities of system modules 130 described in Figures 2-3. Instructions 624 may also reside, completely or at least partially, within main memory 604 or within processor 602 (e.g., within processor cache memory) during their execution by computer system 600, main memory 604, and processor 602 also constituting machine-readable media. Instructions 624 may be transmitted or received over a network 626 (e.g., network 220) via network interface device 620.

V. ADDITIONAL CONSIDERATIONS

[00136] In the above description, for purposes of explanation, numerous specific details are presented to provide a complete understanding of the illustrated system and its operations. It will be apparent, however, to one skilled in the art that the system can be operated without these specific details. In other cases, structures and devices are shown in block diagram form to avoid obscuring the system.

[00137] The reference in the Descriptive Report to “an embodiment” or “an embodiment” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the system. Appearances of the phrase “in an embodiment” in various places in the Descriptive Report do not necessarily refer to the same embodiment.

[00138] Some parts of the detailed descriptions are presented in terms of algorithms or models and symbolic representations of operations on data bits within a computer memory. An algorithm is here, and generally, designed to be steps that lead to a desired result. Steps are those that require physical transformations or manipulations of physical quantities. Typically, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has sometimes proven convenient, mainly for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[00139] It should be borne in mind, however, that all of these and similar terms must be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless otherwise specifically stated as apparent from the following discussion, it is taken into account that throughout the description, discussions using terms such as “processing” or “computation” or “calculation” or “determination” or “display” or similar, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities in the records and memories of the computer system into other data represented in a similar manner as physical quantities in the memories or records of computer system or other such information storage, transmission or display devices.

[00140] Some of the operations described herein are performed by a computer physically mounted within an agricultural machine 100. This computer may be constructed especially for the required purposes, or may comprise a general purpose computer selectively activated or reconfigured by a software program. computer stored on the computer. This computer program may be stored on a computer-readable storage medium such as, but not limited to, any type of disk, including floppy disks, optical disks, CD-ROMs and optical-magnetic disks, read-only memories (ROMs | only memories), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of non-transitory computer-readable storage medium suitable for storing electronic instructions.

[00141] The figures and description above refer to various embodiments for illustration purposes only. It should be noted that, from the following discussion, alternative embodiments of the structures and methods disclosed herein will be readily recognized as viable alternatives that can be employed without departing from the principles of what is claimed.

[00142] One or more embodiments were described above, examples of which are illustrated in the attached figures. It is noted that where possible similar or similar reference numbers may be used in the figures and may indicate similar or similar functionality. The figures represent embodiments of the disclosed system (or method) for illustration purposes only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein.

[00143] Some modalities can be described using the expressions “coupled” and “connected” together with their derivatives. It should be understood that these terms are not synonymous with each other. For example, some embodiments may be described using the term “connected” to indicate that two or more elements are in direct physical or electrical contact with each other. In another example, some embodiments may be described using the term “coupled” to indicate that two or more elements are in direct physical or electrical contact. The term “coupled”, however, can also mean that two or more elements are not in direct physical or electrical contact with each other, but still cooperate or interact with each other. The modalities are not limited in this context.

[00144] As used herein, the terms “comprises”, “comprising”, “includes”, “including”, “has”, “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article or apparatus comprising a list of elements is not necessarily limited to those elements only, but may include other elements not expressly listed or inherent in such process, method, article or apparatus. Furthermore, unless expressly stated otherwise, “or” refers to an inclusive or and not an exclusive or. For example, a condition A or B is satisfied by any of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).

[00145] Furthermore, the use of “a” or “an” is employed to describe elements and components of the modalities presented here. This is done for convenience only and to give a general idea of the system. This description should be read to include one or at least one and the singular also includes the plural unless it is obvious that it means otherwise.

[00146] Upon reading this invention, those skilled in the art will appreciate additional alternative structural and functional designs for a system and a process for identifying and treating plants with an agricultural machine including a control system that executes a semantic segmentation model. Thus, although particular embodiments and applications have been illustrated and described, it should be understood that the disclosed embodiments are not limited to the precise construction and components disclosed herein. Various modifications, alterations and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope defined in the attached Claims.

Claims (20)

1. Method for Agricultural Machine in Field, to dynamically adjust treatment buffer during execution of treatment of plants in the field, characterized in that the method comprises: accessing an image of the field comprising pixels, the pixels comprising pixels representing a plant in the field ; receiving sensor signals from one or more sensors coupled to the agricultural machine, the sensor signals comprising representations of positioning information from a handling mechanism of the agricultural machine; applying the image and sensor signals to a computer model configured to determine a spatial relationship between the treatment mechanism and the plant, the computer model generating an uncertainty measurement for an expected position of the treatment mechanism relative to an expected position of the plant; adjusting the treatment buffer based on the uncertainty measurement for the expected position of the treatment mechanism; and treating the plant in the field by applying the plant treatment to the plant according to the adjusted treatment buffer. 2. Method for Agricultural Machine in Field, according to Claim 1, characterized in that it further comprises: determining that the uncertainty measurement exceeds a limit uncertainty value; and responsive to the determination of uncertainty measurement exceeds the uncertainty threshold value, adjust the treatment buffer to convey the plant treatment. 3. Method for Agricultural Machine in Field according to Claim 1, characterized in that the plant treatment is a fluid, the treatment buffer is a spray buffer, the treatment mechanism comprises a spray nozzle and adjusting the buffer treatment comprises one or more of: adjusting a duration for which the spray nozzle sprays fluid; adjusting a number of activated spray nozzles on the treatment mechanism; adjust a spray nozzle opening; and adjusting a spray nozzle pulse width modulation duty cycle. 4. Method for Agricultural Machine in Field, according to Claim 1, characterized in that the sensor signals further comprise representations of external condition information, the method further comprising: adjusting the treatment buffer based on the sensor signals that represent external condition information. 5. Method for Agricultural Machine in Field, according to Claim 1, characterized in that it further comprises: receiving manual activation instructions to adjust the treatment buffer to a specific configuration; adjust the processing buffer for the specific configuration; and locking the treatment buffer to the specific configuration so that the specific configuration overrides additional adjustments based on new uncertainty measurements. 6. Method for Agricultural Machine in Field, according to Claim 1, characterized in that adjusting the treatment buffer based on the uncertainty measurement further comprises: determining a corresponding bucket of buckets that group uncertainty measurement values in different ranges, wherein each bucket is associated with a different treatment buffer configuration and the corresponding bucket includes an uncertainty measurement value in a respective range of uncertainty measurement values; and responsive to determine the corresponding bucket, adjust the processing buffer to the processing buffer configuration associated with the corresponding bucket. 7. Method for Agricultural Machine in Field, according to Claim 1, characterized in that it further comprises: accessing a new image of the field captured after the image in the field; determining a new uncertainty measurement based on the new image that is less than the uncertainty measurement; and adjust the treatment buffer to cover a smaller area of the field. 8. Method for Agricultural Machine in Field, according to Claim 1, characterized in that it further comprises: accessing a new image of the field captured after the image in the field; determining a new uncertainty measurement based on the new image that is greater than the uncertainty measurement; and adjust the treatment buffer to cover a larger area of the field. 9. Method for Agricultural Machine in Field, according to Claim 1, characterized in that it further comprises one or more of the following: recording the adjusted treatment buffer in a log; and reporting the adjusted processing buffer to a remote system. 10. Method for Agricultural Machine in Field, according to Claim 1, characterized in that it further comprises: generating a user interface that visually represents the treatment buffer. 11. Method for Agricultural Machine in Field, according to Claim 10, characterized in that it further comprises: sending the generated user interface to a user device for display; receiving user input to adjust the processing buffer through the generated user interface; and adjust the processing buffer according to the user input received. 12. Method for Agricultural Machine in Field, according to Claim 1, characterized in that it further comprises: varying a plant treatment application rate based on measurement of uncertainty. 13. Method for Agricultural Machine in Field, according to Claim 1, characterized in that applying the image and sensor signals to the computer model comprises: identifying a set of pixels of the accessed image as the plant; determining, based on sensor signals, an expected position of the agricultural machine's treatment mechanism; determining an uncertainty measurement for the expected position of the treatment mechanism; and determining, based on the set of pixels and the expected position of the treatment mechanism, an expected position of the identified plant. 14. Method for Agricultural Machine in Field, according to Claim 1, characterized in that it further comprises: generating, based on the expected position of the treatment mechanism, the expected position of the identified plant and the adjusted treatment buffer, machine instructions for the agricultural machine to: position the treatment mechanism of the agricultural machine to target the identified plant in the field and carry out plant treatment for the identified plant using the positioned treatment mechanism and the adjusted treatment buffer; where plant treatment in the field is based on generated machine instructions. 15. Agricultural Machine, characterized in that it comprises: a controller for: accessing an image of the field comprising pixels, the pixels comprising pixels representing a plant in the field; receiving sensor signals from one or more sensors coupled to the agricultural machine, the sensor signals comprising representations of positioning information from a handling mechanism of the agricultural machine; applying the image and sensor signals to a computer model configured to determine a spatial relationship between the treatment mechanism and the plant, the computer model generating an uncertainty measurement for an expected position of the treatment mechanism relative to an expected position of the plant; adjusting the treatment buffer based on the uncertainty measurement for the expected position of the treatment mechanism; and the treatment mechanism, configured to treat the plant in the field by applying the plant treatment to the plant according to the adjusted treatment buffer. 16. Agricultural Machine according to Claim 15, characterized in that the plant treatment is a fluid, the treatment buffer is a spray buffer, the treatment mechanism comprises a spray nozzle, and the adjustment of the treatment buffer comprises one or more of: adjusting a duration for which the spray nozzle sprays fluid; adjusting a number of activated spray nozzles on the treatment mechanism; adjust a spray nozzle opening; and adjusting a spray nozzle pulse width modulation duty cycle. 17. Agricultural Machine, according to Claim 15, characterized in that the sensor signals further comprise representations of external condition information, the controller further to: adjust the treatment buffer based on the sensor signals representing external condition information . 18. Agricultural Machine, according to Claim 15, characterized in that the controller is further to: access a new image of the field captured after the image in the field; determining a new uncertainty measurement based on the new image that is greater than the uncertainty measurement; and adjust the treatment buffer to cover a larger area of the field. 19. Agricultural Machine, according to Claim 15, characterized in that applying the image and sensor signals to the computer model comprises: identifying a set of pixels of the accessed image as the plant; determining, based on sensor signals, an expected position of the agricultural machine's treatment mechanism; determining an uncertainty measurement for the expected position of the treatment mechanism; and determining, based on the set of pixels and the expected position of the treatment mechanism, an expected position of the identified plant. 20. Agricultural Machine, according to Claim 15, characterized in that the controller is further to: generate, based on the expected position of the treatment mechanism, the expected position of the identified plant and the adjusted treatment buffer, machine instructions for the agricultural machine to: position the treatment mechanism of the agricultural machine to reach the identified plant in the field and carry out plant treatment for the identified plant using the positioned treatment mechanism and the adjusted treatment buffer; where plant treatment in the field is based on generated machine instructions.