CN113686848A - Plant state judging method - Google Patents

Plant state judging method Download PDF

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Publication number
CN113686848A
CN113686848A CN202010425440.7A CN202010425440A CN113686848A CN 113686848 A CN113686848 A CN 113686848A CN 202010425440 A CN202010425440 A CN 202010425440A CN 113686848 A CN113686848 A CN 113686848A
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Prior art keywords
plant
abnormal
image data
sensing data
normal
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CN202010425440.7A
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Chinese (zh)
Inventor
锺镇宇
余远鋆
翁翊涵
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Delta Electronics Inc
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Delta Electronics Inc
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Priority to CN202010425440.7A priority Critical patent/CN113686848A/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • G01N2021/0106—General arrangement of respective parts
    • G01N2021/0118—Apparatus with remote processing
    • G01N2021/0125—Apparatus with remote processing with stored program or instructions
    • G01N2021/0131—Apparatus with remote processing with stored program or instructions being externally stored
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N2021/8411—Application to online plant, process monitoring
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N2021/8466—Investigation of vegetal material, e.g. leaves, plants, fruits

Abstract

A plant status determination method, comprising: shooting image data of a plant to be detected through an image shooting device; acquiring sensing data of a plant to be detected or a growing environment through a sensor; analyzing the object to be measured by an analyzing device according to the normal database, the image data, the sensing data and the growth stage information, wherein the growth stage information comprises the type, the category and the planting period of the plant to be measured; judging whether the plant to be detected is in a normal state or not through an analysis device; when the judgment result is yes, ending the current operation; and when the judgment result is 'no', judging the plant to be detected to be in an abnormal state through the analysis device according to the symptom database, the abnormal image data, the abnormal sensing data and the growth stage information, and generating an abnormal result.

Description

Plant state judging method
Technical Field
Embodiments described in the present disclosure relate to a method for determining plant status, and more particularly, to a method for determining plant status that can determine whether the content of growth elements is abnormal.
Background
In the related art in the field of plants, there are several ways to determine whether a plant is abnormal. For example, there is a method of using human judgment, but human judgment is prone to misjudgment or subjective judgment. Another way is to purchase specialized equipment to make the determination, but the purchase of the equipment is expensive. Yet another way is to delegate others to assist in the inspection, but such a way is not judged in real time.
Disclosure of Invention
Some embodiments of the present disclosure relate to a plant status determination method. The plant state judging method comprises the following operation steps: shooting image data of a plant to be detected by an image shooting device; obtaining sensing data of the plant to be detected or the growing environment through a sensor; analyzing the object to be measured by an analysis device according to a normal database, image data, sensing data and growth stage information of the plant to be measured, wherein the growth stage information comprises the type, the category and the planting period of the plant to be measured; judging whether the plant to be detected is in a normal state or not through the analysis device, setting the image data as normal image data and setting the sensing data as normal sensing data when the judgment result is yes, and finishing the current operation; and when the judgment result is 'no', setting the image data as abnormal image data, setting the sensing data as abnormal sensing data, and judging the plant to be detected to be in an abnormal state through the analysis device according to a symptom database, the abnormal image data, the abnormal sensing data and the growth stage information; and generating an exception result.
In some embodiments, the plant state determination method further comprises the following operation steps: when the result of judging whether the plant to be detected is in a normal state is yes, establishing or updating the normal image data and the normal sensing data in a normal database; and when the result of judging whether the plant to be detected is in a normal state is 'no', establishing or updating the abnormal image data and the abnormal sensing data in a symptom database.
In some embodiments, the abnormal image data and the abnormal sensing data correspond to an element-excess state or an element-deficiency state.
In some embodiments, the plant state determination method further comprises the following operation steps: at least one abnormal reason is analyzed according to the abnormal result, wherein the abnormal reason is updated to the symptom database.
In some embodiments, the plant state determination method further comprises the following operation steps: and adjusting the planting conditions of the next batch of plants according to the abnormal result and the abnormal reason.
In some embodiments, the image capturing device captures image data of the plant based on an image capturing parameter.
In some embodiments, the image capturing parameters include at least one of a position, a focal length, a capturing angle, a capturing depression angle, a color temperature, a hue, a contrast, a brightness, and a lens type of the image capturing device.
In some embodiments, the plant state determination method further comprises the following operation steps: judging whether an abnormal area in the image data is larger than a preset area or not; and if the judgment result is 'yes', judging that the plant to be detected is in an abnormal state.
In some embodiments, the analyzing device performs the process of analyzing the image data according to a color standard.
In some embodiments, the sensed data corresponding to the plant itself comprises chlorophyll content, plant weight, or leaf brittleness, and the sensed data corresponding to the growing environment comprises medium ph, ambient temperature, or ambient light intensity.
In summary, the plant state determination method in the disclosure can automatically determine the normal state or the abnormal state of the plant, reduce the false determination probability of human determination, and have the advantages of low cost and real-time determination.
Drawings
In order to make the aforementioned and other objects, features, advantages and embodiments of the disclosure more comprehensible, the following description is given:
FIG. 1 is a schematic diagram illustrating a plant status determination system according to some embodiments of the present disclosure;
FIG. 2 is a flow chart of a method for determining plant status according to some embodiments of the present disclosure; and
FIG. 3 is a schematic diagram illustrating an operator interface according to some embodiments of the present disclosure.
Description of reference numerals:
100: plant state judging system
110: image shooting device
120: analysis device
130: sensor device
140: operation interface
PLA: plant to be tested
DB 1: database with a plurality of databases
DB 2: database with a plurality of databases
IMG: image data
IMGnor: normal image data
IMGabn: abnormal image data
SDATA: sensing data
SDATAnor: normal sensing data
SDATAabn: abnormal sensing data
STAGE: growth stage information
And RS: abnormal result
200: plant state judging method
S202-S220: procedure for the preparation of the
P1: selection window
P2: selection window
P3: display window
RE: cause of abnormality
ELM: abnormal image data of element deficiency
Detailed Description
The following detailed description is provided by way of example only and with reference to the accompanying drawings, which are not intended to limit the scope of the present disclosure, but rather are intended to limit the order of execution of the embodiments, and any arrangement of components or structures which results in a technically equivalent result is intended to be within the scope of the present disclosure. In addition, the drawings are for illustrative purposes only and are not drawn to scale. For ease of understanding, the same or similar elements will be described with the same reference numerals in the following description.
The term "coupled", as used herein, may also mean "electrically coupled", and the term "connected", as used herein, may also mean "electrically connected". "coupled" and "connected" may also mean that two or more elements co-operate or interact with each other.
Refer to fig. 1. Fig. 1 is a schematic diagram illustrating a plant status determination system 100 according to some embodiments of the present disclosure. Referring to fig. 1, the plant status determination system 100 includes an image capturing device 110, an analysis device 120, one or more sensors 130, an operation interface 140, a database DB1, and a database DB 2.
The image capturing device 110 is electrically coupled to the analysis device 120. The analysis device 120 is electrically coupled to the sensor 130, the operation interface 140, the database DB1 and the database DB2, respectively. The image capturing device 110 may be, for example, a camera, and is mounted on a robot arm (not shown), but the disclosure is not limited thereto. The analysis device 120 may be, for example, a device including a processor or a micro-processing unit (MCU). The sensor 130 may be, for example, a sweetmeter, a conductivity meter, a chlorophyll meter, a weight sensor, a crispmeter, an acid-base meter, a thermometer, a hygrometer or a photometer for sensing at least one sensed data of the plant itself or the growing environment. The operation interface 140 may be, for example, a touch display panel.
The configurations or implementations of the image capturing device 110, the analyzing device 120, the sensor 130, and the operation interface 140 are only examples, and various combinations, configurations, or implementations of the configurations and implementations are within the scope of the present disclosure. In addition, in some other embodiments, the database DB1 and the database DB2 may be integrated into a single database or implemented by one server, and distinguished by different storage spaces or modules.
Fig. 2 is a flow chart of a plant status determination method 200 according to some embodiments of the present disclosure. In some embodiments, the plant status determination method 200 is applied to the plant status determination system 100 of fig. 1, but the disclosure is not limited thereto. As shown in fig. 2, the plant status determination method 200 includes operations S202 to S220. For ease of understanding, the plant status determination method 200 will be discussed in conjunction with fig. 1.
In some pre-operation steps, a database DB1 and a database DB2 may be established. In some embodiments, hydroponic techniques can be used to grow normal plants, abnormal plants that are over-elemental (e.g., over-calcium), and abnormal plants that are under-elemental (e.g., under-calcium), respectively. In practice, the elements (such as nutrient solution or air) of the plants can be precisely controlled by a hydroponic technique. The normal plants and the abnormal plants are used to pre-establish the data in the database DB1 and the database DB2, so that the plant status determination system 100 can directly use the data in the future to determine the abnormal status of the plant PLA to be tested, and even provide the possible reasons for the abnormal status for the user. In addition, in plant factories, elements necessary for plant growth include chemical components such as calcium, magnesium, phosphorus, and … supplied through nutrient solution, and elements in a broad sense include light, air, and heat …, and the abnormality of the plant growth state can be observed and a database can be built.
Then, the image capturing device 110 captures the normal plant to obtain normal image data of the normal plant, and the sensor 130 obtains sensed data of the normal plant itself or the growing environment. The normal image data and the normal sensing data are stored in the database DB 1. In some embodiments, database DB1 may be referred to as a normal database (referred to below as normal database DB1 for ease of illustration). In addition, the image capturing device 110 can capture the abnormal plant with excessive elements to obtain at least one element excessive abnormal image data, and the sensor 130 obtains at least one element excessive abnormal sensing data of the abnormal plant with excessive elements or the growing environment. Furthermore, the image capturing device 110 can also capture the abnormal plant with the element deficiency to obtain at least one abnormal image data with the element deficiency, and the sensor 130 obtains at least one abnormal sensing data with the element deficiency of the abnormal plant itself or the growing environment. Finally, the above-mentioned excessive element abnormal image data, excessive element abnormal sensed data, insufficient element abnormal image data, and insufficient element abnormal sensed data are stored in the database DB 2. In some embodiments, the database DB2 may be referred to as a symptom database (hereinafter referred to as symptom database DB2 for ease of description).
In some other embodiments, the normal DB1 and/or the symptom DB2 are established based on different stages of the plant growing process. For example, the growth process of a planted plant can be roughly divided into a seeding stage, a seedling stage, a temporary planting stage, a permanent planting stage and a harvesting stage. Wherein, the same planting plate (for example, the distribution density of the planting holes is larger) can be adopted in the seeding stage and the seedling raising stage, the other planting plate (for example, the distribution density of the planting holes is medium) can be adopted in the temporary planting stage, and the other planting plate (for example, the distribution density of the planting holes is smaller) can be adopted in the fixed planting stage and the harvesting stage. Accordingly, the normal image data and the normal sensing data corresponding to all or part of the stages may be stored in the normal database DB1, and the excessive abnormal image data, the excessive abnormal sensing data, the insufficient abnormal image data, and the insufficient abnormal sensing data corresponding to all or part of the stages may be stored in the syndrome database DB2, so as to establish the normal database DB1 and the syndrome database DB2, respectively.
In some pre-processing steps, a preliminary determination may be made as to whether a batch of plants is normal. The plants in the batch for judgment are preferably all at the same stage in the growing process and grown in the same planting environment, but the disclosure is not limited thereto. For example, the image capturing device 110 captures at least one initial image data covering all or part of the batch of plants. Then, the analysis device 120 analyzes and determines whether the area occupied by the abnormal plant in the initial image data (e.g., the area occupied by the plant with abnormal color) is larger than a preset area for planting (e.g., the area of the planting plate or the planting bed, or the area covered by the peripheral leaf margin). If not, the batch of plants is judged to be normal and the process is ended. If so, namely the abnormal area is larger than the preset area, judging that the batch of plants are abnormal. Next, the operation proceeds to operation S202. In operation S202, the image capturing device 110 is controlled to capture the suspected abnormal plant PLA of the batch of plants, i.e. the plant PLA to be detected is determined for confirmation, and further detection is performed.
In operation S202, as described above, the image capturing device 110 captures the suspected abnormal plant PLA of the batch of plants to obtain the image data IMG of the plant PLA, and transmits the image data IMG to the analysis device 120. In some embodiments, the image capturing device 110 captures the plant PLA to be tested based on an image capturing parameter. Furthermore, the image capturing device 110 may capture the normal plants for establishing the normal database DB1 based on the same image capturing parameters, and capture the abnormal plants with excessive elements and the abnormal plants with insufficient elements for establishing the symptom database DB2 based on the image capturing parameters, so as to improve the accuracy of image comparison. The image capturing parameters may be, for example, a position, a focal length, a capturing angle, a capturing depression angle, a color temperature, a hue, a contrast, a brightness, or a lens type of the image capturing device 110, which is not limited in the present disclosure. For example, a location point may be set on the plant growing plate, so that the image capturing device 110 disposed on the robot arm captures the plants on different plant growing plates at the same capturing angle or the same capturing depression angle. The robot arm is easy to adjust the shooting angle or the shooting depression angle, but the disclosure is not limited thereto. By adopting the same image shooting parameters, the shooting conditions can be kept fixed, so that the influence on the image data due to different shooting conditions is avoided, and the accuracy of image comparison is improved.
In addition, the plant to be tested PLA, the normal plants used to establish the normal database DB1, the abnormal plants used to establish the symptom database DB2 with excessive elements, and the abnormal plants with insufficient elements are grown in the same growth stage under the same environmental conditions (except the elements controlling the factors) so that the environmental conditions of the comparison analysis are fixed (e.g., light, temperature, humidity) to avoid affecting the image data due to different environmental conditions.
Next, in operation S204, the plant PLA to be tested is sensed by the sensor 130 to obtain at least one sensing data SDATA of the plant PLA to be tested itself or the growing environment, and the sensing data SDATA is transmitted to the analysis device 120. As described above, the sensor 130 may be, for example, a chlorophyll meter, a weight sensor, a brittleness meter, an acid-base meter, a thermometer, or a photometer. Accordingly, the sensing data SDATA may correspond to the chlorophyll content of the plant PLA itself to be detected, the plant weight or the leaf brittleness, or correspond to the medium ph value, the environmental temperature or the environmental light intensity of the growing environment, for example, which is not limited by the disclosure.
Next, in operation S206, the analyzing device 120 determines whether the plant PLA to be tested is in a normal state according to the normal database DB1, the image data IMG, the sensing data SDATA, and a current growth STAGE information STAGE of the plant PLA to be tested, where the growth STAGE information STAGE includes a type, a category, and a planting STAGE (such as seedling, temporary planting, permanent planting, adult plant …) of the plant, and these information may be built in the normal database DB1, the symptom database DB2, or manually inputted and may be updated. For example, the analysis device 120 compares the image data IMG of the plant PLA to be tested with the normal image data stored in the normal database DB1, compares the sensing data SDATA of the plant PLA to be tested with the normal sensing data stored in the normal database DB1, and compares the growth STAGE information STAGE of the plant PLA to be tested, so as to determine whether the plant PLA to be tested is in a normal state, if so, the operation continues to step S208.
In some embodiments, the analyzing device 120 performs the process of analyzing the initial image data or the image data IMG according to a color standard. The color standard may be, for example, a color ticket, but the disclosure is not limited thereto. For example, the color of a block in the image data is compared with the color ticket and converted into a corresponding gray scale value, so as to determine whether the colors are similar or the same by using the converted gray scale value. By means of the color standards, misjudgment due to shooting differences, such as momentary changes in lighting or exposure conditions, can be avoided.
If the determination in operation S208 is yes (i.e. the plant PLA to be tested is in a normal state), the current image data IMG is set as a normal image data IMGnorSetting the current sensing data SDATA to a normal sensing data SDATAnorAnd the procedure can be directly ended, or another plant to be tested PLA can be judged (back)To operation S202). Otherwise, if the determination in operation S208 is "no" (i.e. the plant PLA to be tested is abnormal), the current image data IMG is set as an abnormal image data IMGabnSetting the current sensing data SDATA as an abnormal sensing data SDATAabnAnd proceeds to operation S210.
In operation S210, the analysis device 120 bases on the syndrome database DB2 and the abnormal image data IMGabnAbnormal sensing data SDATAabnAnd judging whether the plant PLA to be detected is in an excessive element state or an abnormal element lack state by the growth STAGE information STAGE, and further generating an abnormal result RS. For example, the analysis device 120 compares the abnormal image data IMG of the plant PLA to be testedabnAbnormal image data corresponding to excessive elements and abnormal image data corresponding to insufficient elements stored in the symptom database DB2, and abnormal sensing data SDATA of the plant PLA to be detected are comparedabnAbnormal sensing data corresponding to the excess of elements and abnormal sensing data corresponding to the deficiency of elements stored in the symptom database DB2, and current growth STAGE information STAGE of the plant PLA to be tested, so as to determine whether the state of the plant PLA to be tested is the excess of elements or the deficiency of elements, and accordingly generate an abnormal result RS. In some embodiments, the abnormal result RS may be stored in and used to update the symptom database DB2 to keep the data in the symptom database DB2 up to date, thereby improving the accuracy of the determination. Next, the operation proceeds to operation S212.
In operation S212, the analysis device 120 processes the abnormal result RS and the abnormal image data IMGabnAnd abnormal sensing data SDATAabnThe output is to the operation interface 140, and the sensor 130 can also directly output the sensing data SDATA to the operation interface 140. The operation interface 140 can receive the abnormal result RS and the abnormal image data IMGabnAnd abnormal sensing data SDATAabnAnd displaying the data for the user to continue to manually inspect and interpret.
Specifically, in operation S206 and operation S210, the leaf contour, the plant color, the vein color, the mesophyll color, the symptom position, the symptom appearance, the area size of the young leaf and the old leaf, the plant diameter, the plant height, the plant volume, or other image features in the two image data IMGs may be compared. In addition, the specific operation of comparing the sensed data may compare the sensed data SDATA such as chlorophyll content, plant weight, leaf brittleness, medium ph, ambient temperature, or ambient light intensity.
It should be noted that there may be other operation steps to update the normal database DB1 and the symptom database DB2 according to the determination result of the operation step S208. In detail, when the determination result in the operation S208 is yes, the operation S214 may be executed again to apply the normal image data IMG corresponding to the plant PLA to be detectednorAnd normal sensing DATA DATAnorStored in the normal database DB 1; otherwise, when the determination result in the operation S208 is "no", the operation S216 may be executed again to apply the abnormal image data IMG corresponding to the plant PLA to be detectedabnAnd abnormal sensing DATA DATAabnStored in the symptom database DB 2. Thus, the normal database DB1 and the abnormal database DB2 can be updated continuously, thereby improving the accuracy of subsequent determination.
In addition, the following operation steps may be further included after the operation step S212. First, operation S218 is continued to analyze at least one RE according to the abnormal result RS and update the syndrome database DB2, i.e., store the RE in the syndrome database DB 2. Then, the operation S220 may be continuously performed to adjust the planting conditions of the next batch of plants according to the abnormal result RS and the abnormal reason RE. The method not only can automatically judge and reduce the probability of human misjudgment, but also can continuously adjust the database to improve the judgment accuracy, and the produced abnormal result RS and the abnormal reason RE can also be directly reflected in the subsequent planting condition, so that the planting condition can be monitored in real time and can also be adjusted at any time.
The above embodiments of comparing the image data and the sensing data are only for illustrative purposes, and various embodiments of comparing the image data and the sensing data are within the scope of the disclosure, which is not limited thereto.
Refer to fig. 2 and 3 together. FIG. 3 is a schematic diagram of an operator interface 140 shown in accordance with some embodiments of the present disclosure. For the example of FIG. 3, the operation interface 140 includes a selection window P1, a selection window P2, and a display window P3. The selection window P1 allows the user to select the type and category of the plant PLA to be tested. For example, the selection window P1 allows the user to select whether the plant PLA to be tested is a plant of the family Brassicaceae, lettuce, or the like. The selection window P2 allows the user to select what part of the plant PLA to be detected. For example, the selection window P2 allows the user to select whether to detect a new leaf (usually at the top or high) or an old leaf (usually at the bottom or low). The display window P3 can display the judgment result of the plant status judgment 100 and related information. For example, the display window P3 may display the image data IMG of the plant PLA to be tested, the element similar to the image data IMG compared in the syndrome database DB2, the absent abnormal image data ELM, and the abnormal result RS determined by operation S216 in fig. 2. In some other embodiments, the operation interface 140 can also display the sensing data SDATA from the sensor 130.
Referring to fig. 3 for illustration, in some embodiments, the analysis device 120 further analyzes at least one abnormal cause RE according to the abnormal result RS, and the display window P3 of the operation interface 140 is further used for displaying the abnormal cause RE. For example, the cause of calcium deficiency in plants may be exchangeable calcium deficiency. Accordingly, the correspondence between plant calcium deficiency and exchangeable calcium deficiency can be pre-stored in a dedicated database or integrated into the database DB 2. When the abnormal result RS is the plant calcium deficiency, the display window P3 provides the abnormal cause RE of exchangeable calcium deficiency for the user to refer to as the basis for adjusting the nutrient solution formula.
The configuration and content of the selection window P1, the selection window P2, and the display window P3 are for illustrative purposes only, and various configurations and content of the selection window P1, the selection window P2, and the display window P3 are within the scope of the present disclosure.
In some embodiments, the plant status determination system 100 can adjust the planting condition of the next batch of plants according to the abnormality cause RE. For example, if the display window P3 provides the abnormal reason RE for the exchangeable calcium deficiency, the pH of the soil or nutrient solution is too high. Accordingly, the plant status determination system 100 can automatically adjust the ph of the soil or nutrient solution of the next plant, thereby avoiding the occurrence of abnormalities after the next plant is planted. Accordingly, the appearance, quality, yield and related benefits of each batch of plants can be maintained within a certain range.
Based on the above, the plant status determination system 100 and the plant status determination method 200 of the present disclosure can avoid human misjudgment, reduce cost, perform real-time detection and non-destructive detection, and are suitable for different plants as subsequent adjustment indexes.
In addition, the plant status determination system 100 and the plant status determination method 200 of the present disclosure can be used to determine whether the complex element is excessive or deficient. For example, in the process of establishing the normal database DB1 and the symptom database DB2, various elements (e.g., calcium, magnesium …, etc.) may be arranged and combined, so as to deliberately cultivate or plant abnormal plants with excessive elements and abnormal plants with insufficient elements according to the arranged and combined elements, so as to store the corresponding image data and sensing data in the symptom database DB2, thereby increasing the data enrichment. Accordingly, the plant status determination system 100 and the plant status determination method 200 can determine that the plant PLA to be tested has taken too much and/or lacks a plurality of elements (e.g., calcium and magnesium deficiency) at the same time.
The above description of the plant status determination method 200 includes exemplary operations, but the operations of the plant status determination method 200 are not necessarily performed in the order shown. It is within the spirit and scope of the embodiments of the present disclosure that the order of the operations of the plant status determination method 200 may be changed, or the operations may be performed simultaneously, partially simultaneously, or partially omitted, where appropriate.
In some embodiments, the plant status determination method 200 can be implemented as a computer program. When the computer program is executed by a processor, a computer or an electronic device, the executing device executes the plant status determining method 200. The computer program can be stored in a non-transitory computer readable recording medium, such as a rom, a flash memory, a floppy disk, a hard disk, an optical disk, a flash disk, a usb disk, a magnetic tape, a database readable from a network, or any other recording medium with the same functions as those of the present disclosure.
In summary, the plant state determination method in the disclosure can automatically determine the normal state or the abnormal state of the plant, reduce the false determination probability of human determination, and have the advantages of low cost and real-time determination.
Although the present disclosure has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the disclosure, and therefore, the scope of the disclosure should be limited only by the appended claims.

Claims (10)

1. A plant state judging method comprises the following operation steps:
shooting image data of a plant to be detected by an image shooting device;
obtaining a sensing data of the plant to be detected or the growing environment through a sensor;
analyzing the object to be measured by an analysis device according to a normal database, the image data, the sensing data and growth stage information of the plant to be measured, wherein the growth stage information comprises the type, the category and the planting period of the plant to be measured;
judging whether the plant to be detected is in a normal state or not through the analysis device, setting the image data as normal image data and setting the sensing data as normal sensing data when the judgment result is yes, and finishing the current operation; and
if the judgment result is 'no', the image data is set as abnormal image data, the sensing data is set as abnormal sensing data, the analysis device judges that the plant to be detected is in an abnormal state according to a symptom database, the abnormal image data, the abnormal sensing data and the growth stage information, and an abnormal result is generated.
2. The plant state judging method according to claim 1, further comprising the steps of:
if the plant to be detected is judged to be in a normal state and the judgment result is 'yes', establishing or updating the normal image data and the normal sensing data in the normal database; and
and when the plant to be detected is judged to be in a normal state and the judgment result is 'no', establishing or updating the abnormal image data and the abnormal sensing data in the symptom database.
3. The method according to claim 2, wherein the abnormal image data and the abnormal sensing data correspond to an element-excess state or an element-deficiency state.
4. The method according to claim 2, further comprising, after generating the abnormal result:
at least one abnormal reason is analyzed according to the abnormal result, wherein the abnormal reason is updated to the symptom database.
5. The method according to claim 4, further comprising, after the abnormality cause is updated to the syndrome database:
and adjusting the planting conditions of the next batch of plants according to the abnormal result and the abnormal reason.
6. The method according to claim 1, wherein the image capturing device captures the image data of the plant based on an image capturing parameter.
7. The method of claim 6, wherein the image capturing parameters include at least one of a position, a focal length, a capturing angle, a capturing depression angle, a color temperature, a hue, a contrast, a brightness, and a lens type of the image capturing device.
8. The method according to claim 1, wherein determining whether the plant under test is in a normal state comprises:
judging whether an abnormal area in the image data is larger than a preset area; and
if the judgment result is 'yes', the plant to be detected is judged to be in the abnormal state.
9. The method according to claim 1, wherein the analyzing device is configured to analyze the image data according to a color standard.
10. The method according to claim 1, wherein the sensing data corresponding to the plant itself comprises chlorophyll content, plant weight or leaf brittleness, and the sensing data corresponding to the growing environment comprises medium ph, ambient temperature or ambient light intensity.
CN202010425440.7A 2020-05-19 2020-05-19 Plant state judging method Pending CN113686848A (en)

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CN103136439A (en) * 2011-11-24 2013-06-05 财团法人资讯工业策进会 Plant disease identification method and plant disease identification system
JP2016198054A (en) * 2015-04-10 2016-12-01 コイト電工株式会社 Plant cultivation apparatus
CN106659136A (en) * 2014-03-04 2017-05-10 绿玛瑙有限公司 Systems and methods for cultivating and distributing aquatic organisms
CN106645155A (en) * 2016-12-29 2017-05-10 深圳前海弘稼科技有限公司 Method and device for monitoring plant growth status based on greenhouse environment
CN107357614A (en) * 2017-06-30 2017-11-17 深圳前海弘稼科技有限公司 Parameter updating method and device, planting box and storage medium
CN107844089A (en) * 2017-10-31 2018-03-27 深圳春沐源控股有限公司 A kind of method, system and Cultivate administration system for planting early warning
TWM572120U (en) * 2018-08-01 2019-01-01 正修學校財團法人正修科技大學 Plant caring system

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102473200A (en) * 2009-07-08 2012-05-23 巴斯夫欧洲公司 System for diagnosis of plant anomalies
CN103136439A (en) * 2011-11-24 2013-06-05 财团法人资讯工业策进会 Plant disease identification method and plant disease identification system
CN106659136A (en) * 2014-03-04 2017-05-10 绿玛瑙有限公司 Systems and methods for cultivating and distributing aquatic organisms
JP2016198054A (en) * 2015-04-10 2016-12-01 コイト電工株式会社 Plant cultivation apparatus
CN106645155A (en) * 2016-12-29 2017-05-10 深圳前海弘稼科技有限公司 Method and device for monitoring plant growth status based on greenhouse environment
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