CN116843164A - Agricultural machinery intelligent control system based on image analysis - Google Patents
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- 238000010191 image analysis Methods 0.000 title claims abstract description 18
- 230000012010 growth Effects 0.000 claims abstract description 26
- 230000008635 plant growth Effects 0.000 claims abstract description 19
- 238000001514 detection method Methods 0.000 claims abstract description 9
- 241000196324 Embryophyta Species 0.000 claims description 97
- 238000007726 management method Methods 0.000 claims description 70
- 239000002689 soil Substances 0.000 claims description 37
- 238000005286 illumination Methods 0.000 claims description 19
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 12
- 238000005070 sampling Methods 0.000 claims description 8
- 238000005527 soil sampling Methods 0.000 claims description 7
- 238000005516 engineering process Methods 0.000 claims description 6
- 238000005507 spraying Methods 0.000 claims description 6
- 230000007613 environmental effect Effects 0.000 claims description 4
- 239000000575 pesticide Substances 0.000 claims description 4
- 230000002265 prevention Effects 0.000 claims description 4
- 238000009333 weeding Methods 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000004904 shortening Methods 0.000 claims description 2
- 238000012423 maintenance Methods 0.000 abstract description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G25/00—Watering gardens, fields, sports grounds or the like
- A01G25/16—Control of watering
- A01G25/167—Control by humidity of the soil itself or of devices simulating soil or of the atmosphere; Soil humidity sensors
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
- A01M7/0089—Regulating or controlling systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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Abstract
The invention relates to the technical field of intelligent control. The invention relates to an intelligent control system of agricultural machinery based on image analysis. The system comprises a farmland acquisition unit, a plant management unit and an agricultural machine management unit; the farmland acquisition unit is used for acquiring farmland plant data and dividing areas of the farmland according to the plant data; the plant management unit is used for collecting farmland environment data, predicting plant growth trend by combining the plant data collected by the farmland collection unit with the environment data, setting a control threshold value for the area according to the growth trend prediction data, and positioning the area exceeding the threshold value; according to the invention, the farmland is divided into grid areas, so that the farmland is distributed and managed, the unified use of agricultural machinery for irrigating and deinsectization is avoided, the efficiency of plant maintenance is improved, and the growth detection control threshold value set for one week for the plants is used for realizing that when the plants need to lack moisture or deinsectization, the agricultural machinery is discharged in time for control work.
Description
Technical Field
The invention relates to the technical field of intelligent control, in particular to an intelligent agricultural machinery control system based on image analysis.
Background
Agricultural production is affected by various farmland environmental factors such as temperature, humidity, moisture and the like, and the quality of farmland environment is greatly affected on the yield and quality of agricultural products. The traditional farmland environment management is usually checked manually at all times, and a series of farmland environment monitoring management is carried out according to experience judgment, so that the method lacks certain accuracy, is easy to generate errors, and cannot meet the requirements of large-scale and large-area farmland management;
meanwhile, the agricultural machine is controlled to regularly irrigate and remove the insects on crops at regular intervals, as the areas of the damaged insects and the sunlight irradiated by the plants are different in different farmland positions, the sunlight irradiated can accelerate the water loss of the plants, so that the time for irrigating and removing the insects on different plants is different, if the agricultural machine is controlled to regularly irrigate and remove the insects, the existing plants can be too late, or the water is too lack of the water for some plants, and meanwhile, the agricultural machine is controlled to irrigate and remove the insects too frequently, so that the growth environment of the plants is damaged, the growth rate of the plants is influenced, and therefore, the intelligent agricultural machine control system based on image analysis is provided.
Disclosure of Invention
The invention aims to provide an intelligent control system of an agricultural machine based on image analysis, so as to solve the problems in the background technology.
In order to achieve the above purpose, an intelligent agricultural machinery control system based on image analysis is provided, which comprises a farmland acquisition unit, a plant management unit and an agricultural machinery management unit;
the farmland acquisition unit is used for acquiring farmland plant data and dividing areas of the farmland according to the plant data;
the plant management unit is used for collecting farmland environment data, predicting plant growth trend by combining the plant data collected by the farmland collection unit with the environment data, setting a control threshold value for the area according to the growth trend prediction data, and positioning the area exceeding the threshold value;
the agricultural machine management unit is used for sending the area, which exceeds the threshold value, of the plant management unit to the agricultural machine management end, the agricultural machine management end sends out agricultural machines to the area, which exceeds the threshold value, to control according to the environmental data and the plant data, and the data of the area, which is subjected to control work, is input to the plant management unit to update the control threshold value.
As a further improvement of the technical scheme, the farmland acquisition unit acquires images of the farmland by using cameras carried by the unmanned aerial vehicle, extracts plant data from the images, and divides the farmland into grid areas according to plant varieties.
As a further improvement of the technical scheme, the plant management unit comprises an environment acquisition module and a growth prediction module;
the environment acquisition module is used for acquiring soil data and acquiring normal soil composition data of plants;
the growth prediction module is used for predicting the future growth trend of plants according to soil data and plant data acquired by the environment acquisition module, and screening the prediction data according to the plant data to acquire the predicted state of plant growth after one week.
As a further improvement of the technical scheme, the environment acquisition module is used for independently sampling each area through the soil sampling tool to acquire independent soil data of each area.
As a further improvement of the technical scheme, the plant management unit comprises a threshold setting module, a control determining module and a positioning module;
the control determining module is used for comparing plant data acquired by the farmland acquisition unit after one week with a plant growth prediction state after one week, outputting a control signal for weeding in the current area if the comparison difference is larger than a control threshold value, and outputting a control signal for watering in the current area if the comparison difference is smaller than the control threshold value;
the positioning module is used for receiving the control signals required by the current area and determining the position of the area requiring control.
As a further improvement of the technical scheme, the positioning module adopts a GPS positioning technology to record the coordinates of the area to be controlled.
As a further improvement of the technical scheme, the plant management unit further comprises an illumination detection module, wherein the illumination detection module is used for extracting illumination coverage data of plants according to collected images of farmlands, acquiring illumination temperature data according to collected weather temperature data, analyzing the coverage data by combining the illumination temperature data to acquire plant water loss data, conveying the water loss data to the growth prediction module, and shortening cycle days for acquiring plant growth prediction states.
As a further improvement of the technical scheme, the agricultural machinery management unit comprises an agricultural machinery management module;
the agricultural machine management module is used for receiving the control signals of the control determining module and sending output signals to the agricultural machine management end by combining the coordinates of the control area, and the agricultural machine management end sends out corresponding agricultural machines to control the control area. .
As a further improvement of the technical scheme, the agricultural machine management unit further comprises an agricultural machine control path management module, and the agricultural machine control path management module is used for planning an agricultural machine walking path according to farmland images collected by the farmland collection unit and combined with an agricultural machine spraying range, so that pesticides sprayed by the agricultural machine are uniformly distributed in a control area.
Compared with the prior art, the invention has the beneficial effects that:
this intelligent control system of agricultural machine based on image analysis is through dividing into the net region with the farmland, realize distributing management farmland, avoid unified use agricultural machine to irrigate and deinsectization to the plant, improve the efficiency to the plant maintenance, through setting for the growth detection prevention and cure threshold value for a week to the plant, realize when the plant needs to appear lacking moisture or need deinsectization, in time discharge agricultural machine prevents and cure work, avoid appearing plant lack of water or excessive moisture, influence the growth environment of plant, simultaneously according to the illumination data of farmland position, adjust observation period, make this system be suitable for the farmland in different areas and manage.
Drawings
Fig. 1 is a schematic diagram of the overall structure of the present invention.
The meaning of each reference sign in the figure is:
10. a farmland acquisition unit; 20. a plant management unit; 30. and an agricultural machine management unit.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the present embodiment is directed to an intelligent agricultural machine control system based on image analysis, which includes a farmland acquisition unit 10, a plant management unit 20, and an agricultural machine management unit 30;
the farmland acquisition unit 10 is used for acquiring farmland plant data and dividing the farmland into areas according to the plant data;
the farmland collection unit 10 acquires an image of a farmland by using a camera mounted on an unmanned aerial vehicle, extracts plant data from the image, and divides the farmland into grid areas according to plant varieties. The method comprises the following steps:
designing a route and ensuring that the unmanned aerial vehicle flies according to a preset route, and ensuring that the whole farmland area is shot;
shooting an image of a farmland area;
extracting plant data in the image using image processing algorithms, such as image segmentation and object detection;
and dividing farmlands according to the plant data. The farmland area can be divided into grid areas with equal size by using a grid method, or the farmland area can be dynamically divided according to the density and distribution condition of plants;
plant data are analyzed and classified and recorded according to plant variety.
The plant management unit 20 is used for collecting farmland environment data, predicting plant growth trend by combining the plant data collected by the farmland collection unit 10 with the environment data, setting a control threshold value for the area according to the growth trend prediction data, and positioning the area exceeding the threshold value;
the plant management unit 20 includes an environment collection module and a growth prediction module;
the environment acquisition module is used for acquiring soil data and acquiring normal soil composition data of plants;
the environment acquisition module is used for independently sampling each region through the soil sampling tool to acquire soil data corresponding to each region. The method comprises the following steps:
selecting soil sampling points: selecting a representative soil sampling point in a farmland; preparation of soil sampling tools: a suitable soil sampling tool is selected. Common tools are soil drills or soil drills;
determination of sampling depth: the depth range of the sample is determined according to the study requirements. Collecting surface soil 0-20 cm of a plant management unit, and deep soil plant management unit 20-100 cm of a farmland collection unit;
the sampling method is implemented: the sampling tool is vertically inserted into soil or is sampled in an oblique mode. Collecting a sufficient number of soil samples according to the sampling depth requirement;
soil sample treatment and preservation: the collected soil samples are placed into clean bags or containers for processing as soon as possible. Soil samples at different sampling points can be processed separately or mixed into an overall representative sample, as desired. In order to maintain the quality of the sample, the sample should be preserved properly to avoid pollution and water loss;
analysis of soil data: and carrying out chemical analysis and physicochemical property test on the soil sample. Can measure indexes such as nutrient content, organic matter content, pH value, salinity and the like of soil, and physical and chemical properties such as soil texture, load and the like. The method comprises the steps of carrying out a first treatment on the surface of the
Data interpretation and application: and carrying out statistical analysis on the collected soil data, and carrying out data interpretation and application by combining farmland management practices and crop demands. By analyzing the soil data, the soil fertility status, the water retention capacity, the pH value and the like can be known, and basis is provided for farmland management and crop planting.
The growth prediction module is used for predicting the future growth trend of the plant according to the soil data collected by the environment collection module and the plant data, and screening the prediction data according to the plant data to obtain the predicted state of the plant growth after one week. The method comprises the following steps:
a regression model may be used to build a plant growth prediction model, with soil data and plant data as inputs and plant growth status as outputs; the formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,indicating the growth status of the plant,/->、/>、...、/>Characteristics of soil and plants are represented, such as soil moisture, soil temperature, soil pH, plant height, leaf number, etc. />、/>、/>、...、/>Coefficients representing the model are learned from the training data by a machine learning algorithm.
Performing characteristic engineering on the collected soil data and plant data, and extracting relevant characteristics such as nutrient content, humidity change rate, growth rate of plants and the like of the soil;
and (5) inputting soil data after one week by using the trained model, and predicting. The predicted result may be the growth state of the plant, such as growth rate, health degree, etc.
The plant management unit 20 includes a threshold setting module, a control determining module, and a positioning module;
the threshold setting module is used for setting a control threshold according to the plant growth prediction state after each area is combined with the corresponding week; the control threshold is set at a plant growth rate, which may be expressed as an increase per week, in centimeters.
The control determining module is used for comparing plant data acquired by the farmland acquisition unit 10 after one week with a plant growth prediction state after one week, outputting a control signal for weeding in the current area if the comparison difference is larger than a control threshold value, and outputting a control signal for watering in the current area if the comparison difference is smaller than the control threshold value; the formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,for plant data, & lt + & gt>For predicting the state of plant growth,/->Growth rate, if->>The control threshold value outputs a weeding control signal if ∈><And outputting a control signal of watering when the control threshold value is reached.
The positioning module is used for receiving the control signals required by the current area and determining the position of the area requiring control.
The positioning module adopts GPS positioning technology to record the coordinates of the area to be controlled. GPS positioning technology principle: the GPS global positioning system is a positioning technology based on satellite navigation. The GPS receiver receives the position and time signals from the satellites and determines the position of the receiver by resolving the time differences of propagation of these signals.
The plant management unit 20 further includes an illumination detection module, which is configured to extract illumination coverage data of plants according to the collected images of the farmland, obtain illumination temperature data according to the collected weather temperature data, analyze the coverage data in combination with the illumination temperature data to obtain plant moisture loss data, and transmit the moisture loss data to the growth prediction module, so as to shorten cycle days for obtaining plant growth prediction states. The method comprises the following steps:
preprocessing farmland images, including noise removal, image enhancement, conversion and the like;
calculating the plant coverage area or proportion according to the outline or the area of the plant, and taking the plant coverage area or proportion as the coverage data of illumination on the plant;
the sunshine duration data of the area where the farmland is located is obtained, and can be obtained through calculation according to the geographic position and the date;
obtaining highest temperature and lowest temperature data of the same day;
according to the sunlight duration and the temperature data, calculating an illumination temperature index, wherein the index can reflect the influence of illumination and temperature on the evaporation of the water in the plant. The formula is as follows:
according to the coverage data and the illumination temperature data, calculating the plant water evaporation potential ET at each moment:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing a function, and calculating the evaporation potential of the plant water by adopting a regression model according to specific conditions;
comparing the moisture evaporation potential of the plant with the soil moisture content to obtain moisture loss data:
function ofIs the difference. The moisture loss data may be expressed as a decrease or degree of change in moisture.
The agricultural machine management unit 30 is configured to send the area where the plant management unit 20 is located beyond the threshold value to the agricultural machine management end, where the agricultural machine management end sends out an agricultural machine to control the area beyond the threshold value according to the environmental data and the plant data, and input the area data after the control is completed to the plant management unit 20 to update the control threshold value.
The agricultural machinery management unit 30 includes an agricultural machinery management module;
the agricultural machine management module is used for receiving the control signals of the control determining module and sending output signals to the agricultural machine management end by combining the coordinates of the control area, and the agricultural machine management end sends out corresponding agricultural machines to control the control area. The method comprises the following steps:
receiving control signals and coordinates of a control area;
and sending the output signal to an agricultural machine management end. This may be through a network connection or other communication means to transmit the control signals and the zone coordinates to the farm machine manager. Ensuring accurate and timely transmission of information;
the agricultural machine management end receives and analyzes the output signal. After receiving the control signal and the area coordinates, the agricultural machine management end analyzes the control signal and the area coordinates through corresponding algorithms and logics to determine what type of agricultural machine should be sent out for control work;
and dispatching out corresponding agricultural machinery to control the control area. And selecting proper agricultural machinery to carry corresponding control equipment according to the indication of the agricultural machinery management end, and carrying out operation in a control area.
The agricultural machinery management unit 30 further includes an agricultural machinery control path management module, which is used for planning an agricultural machinery walking path according to the farmland image collected by the farmland collection unit 10 and combining with the agricultural machinery spraying range, so that the agricultural machinery sprayed pesticide is uniformly distributed in the control area. The method comprises the following steps:
extracting the boundary or outline of the control area and the agricultural machinery spraying range by using image segmentation and object detection; the expression is as follows:
threshold segmentation: the threshold segmentation is to binarize the image according to the pixel gray value. Common thresholding methods are global thresholding and local thresholding.
Global threshold segmentation: scene threshold= (maximum gray value + minimum gray value)/2;
binary image = image gray value > scene threshold;
local thresholding: a local threshold is calculated from the gray values of the neighborhood around the pixel.
Based on the boundary of the control area and the spraying range of the agricultural machinery, a path planning algorithm is used to determine the agricultural machinery travel path so as to achieve uniform coverage of the whole control area. The path planning algorithm can optimize path design according to vegetation distribution, agricultural machinery spraying range and other information in farmland images; the path planning algorithm formula is as follows:
a comprehensive evaluation value indicating a node n; />Representing the actual cost from the initial node to node n, this value representing the actual consumption from the initial node to the current node n, being a known (measurable) cost; />Representing a heuristic estimated cost from node n to the target node, this value is a heuristic function for estimating the cost from the current node n to the target node, which is not the actual cost but an approximation estimated from the nature of the problem and the design of the heuristic function.
The algorithm comprehensively considers two factors: the actual cost and the heuristic estimated cost,reflects the known actual cost, and +.>A heuristic evaluation cost is provided, by summing the two values, a comprehensive evaluation value +.>For selecting the next node most likely to result in the best path.
According to the path planning result, the agricultural machinery is controlled to walk according to the planned path through automatic navigation of the agricultural machinery, so that the pesticide is uniformly sprayed.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. Agricultural machine intelligent control system based on image analysis, its characterized in that: comprises a farmland acquisition unit (10), a plant management unit (20) and an agricultural machine management unit (30);
the farmland acquisition unit (10) is used for acquiring farmland plant data and dividing areas of the farmland according to the plant data;
the plant management unit (20) is used for collecting farmland environment data, predicting plant growth trend by combining the plant data collected by the farmland collection unit (10) with the environment data, setting a control threshold value for the area according to the growth trend prediction data, and positioning the area exceeding the threshold value;
the agricultural machine management unit (30) is used for sending the area, which exceeds the threshold value, of the plant management unit (20) to the agricultural machine management end, the agricultural machine management end sends out the agricultural machine to the area which exceeds the threshold value according to the environmental data and the plant data to control, and the data of the area after the control is finished is input to the plant management unit (20) to update the control threshold value.
2. The intelligent control system for agricultural machinery based on image analysis according to claim 1, wherein: the farmland acquisition unit (10) acquires images of a farmland by using cameras carried by the unmanned aerial vehicle, extracts plant data from the images, and divides the farmland into grid areas according to plant varieties.
3. The intelligent control system for agricultural machinery based on image analysis according to claim 1, wherein: the plant management unit (20) comprises an environment acquisition module and a growth prediction module;
the environment acquisition module is used for acquiring soil data and acquiring normal soil composition data of plants;
the growth prediction module is used for predicting the future growth trend of plants according to soil data and plant data acquired by the environment acquisition module, and screening the prediction data according to the plant data to acquire the predicted state of plant growth after one week.
4. The intelligent control system for agricultural machinery based on image analysis according to claim 3, wherein: the environment acquisition module is used for independently sampling each region through a soil sampling tool to acquire soil data corresponding to each region.
5. The intelligent control system for agricultural machinery based on image analysis according to claim 4, wherein: the plant management unit (20) comprises a threshold setting module, a control determining module and a positioning module;
the threshold setting module is used for setting a control threshold according to the plant growth prediction state after each area is combined with the corresponding week;
the control determining module is used for comparing plant data acquired by the farmland acquisition unit (10) after one week with a plant growth prediction state after one week, outputting a control signal for weeding in the current area if the comparison difference is larger than a control threshold value, and outputting a control signal for watering in the current area if the comparison difference is smaller than the control threshold value;
the positioning module is used for receiving the control signals required by the current area and determining the position of the area requiring control.
6. The intelligent control system for agricultural machinery based on image analysis according to claim 5, wherein: the positioning module records the coordinates of the area to be controlled by adopting a GPS positioning technology.
7. The intelligent control system for agricultural machinery based on image analysis according to claim 1, wherein: the plant management unit (20) further comprises an illumination detection module, wherein the illumination detection module is used for extracting illumination coverage data of plants according to collected images of farmlands, acquiring illumination temperature data according to collected weather temperature data, analyzing the coverage data in combination with the illumination temperature data to acquire plant water loss data, conveying the water loss data to the growth prediction module, and shortening cycle days for acquiring plant growth prediction states.
8. The intelligent control system for agricultural machinery based on image analysis according to claim 5, wherein: the agricultural machinery management unit (30) comprises an agricultural machinery management module;
the agricultural machine management module is used for receiving the control signals of the control determining module and sending output signals to the agricultural machine management end by combining the coordinates of the control area, and the agricultural machine management end sends out corresponding agricultural machines to control the control area.
9. The intelligent control system for agricultural machinery based on image analysis according to claim 1, wherein: the agricultural machinery management unit (30) further comprises an agricultural machinery prevention and control path management module, and the agricultural machinery prevention and control path management module is used for planning an agricultural machinery walking path according to farmland images collected by the farmland collection unit (10) and combining with an agricultural machinery spraying range, so that pesticides sprayed by the agricultural machinery are uniformly distributed in a prevention and control area.
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| CN117268476A (en) * | 2023-11-21 | 2023-12-22 | 连云港华企立方信息技术有限公司 | Intelligent management method based on ecological agriculture |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US20150237790A1 (en) * | 2013-03-07 | 2015-08-27 | Blue River Technology, Inc. | System and method for automated odometry calibration for precision agriculture systems |
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| CN110050619A (en) * | 2019-05-10 | 2019-07-26 | 广西润桂科技有限公司 | Unmanned plane sugarcane prevention and control of plant diseases, pest control spray method based on accurate meteorological support technology |
| CN116391690A (en) * | 2023-04-24 | 2023-07-07 | 北星空间信息技术研究院(南京)有限公司 | Intelligent agricultural planting monitoring system based on big data of Internet of things |
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| US20150237790A1 (en) * | 2013-03-07 | 2015-08-27 | Blue River Technology, Inc. | System and method for automated odometry calibration for precision agriculture systems |
| CN109191074A (en) * | 2018-08-27 | 2019-01-11 | 宁夏大学 | Wisdom orchard planting management system |
| CN110050619A (en) * | 2019-05-10 | 2019-07-26 | 广西润桂科技有限公司 | Unmanned plane sugarcane prevention and control of plant diseases, pest control spray method based on accurate meteorological support technology |
| CN116391690A (en) * | 2023-04-24 | 2023-07-07 | 北星空间信息技术研究院(南京)有限公司 | Intelligent agricultural planting monitoring system based on big data of Internet of things |
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| CN117268476A (en) * | 2023-11-21 | 2023-12-22 | 连云港华企立方信息技术有限公司 | Intelligent management method based on ecological agriculture |
| CN117268476B (en) * | 2023-11-21 | 2024-03-22 | 连云港华企立方信息技术有限公司 | Intelligent management method based on ecological agriculture |
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