Plant growth monitoring system based on big data

Abstract

The invention discloses a plant growth monitoring system based on big data, and relates to the field of plant growth monitoring, the system is connected with a server, and the server is internally provided with a data acquisition unit, a shape observation module, a loop analysis module, an internal shape measurement and analysis module and a comprehensive state early warning module. The method comprises the following steps: analyzing a plant appearance image to obtain the ratio of the number of plants growing with abnormal color signs in the total plant, the total growth state value of the plant and the total external growth state value of the plant, analyzing plant environment information to obtain the total annular estimated value of the plant, and judging and analyzing the internal content index of the plant to obtain the internal finger value of the plant and the total internal state value of the plant. The plant total state value is obtained by analyzing the plant total state value, so that the appearance condition of the plant can be analyzed in time, the external growth state and the abnormal condition of the plant can be known, the environmental information of various plants can be analyzed, the internal content index of the plant can be analyzed, and the growth state of the plant can be comprehensively judged; the normal growth of the plants can be better ensured.

Description

Technical Field

[0001] The present invention relates to the field of plant growth monitoring, and in particular to a plant growth monitoring system based on big data. Background Art

[0002] With the increasing development of modernization, the demand for planting industry is increasing day by day, and the quality and safety requirements of planting industry are also getting higher and higher. Traditional plant growth monitoring is too dependent on the quality of personnel, and it is difficult to detect the growth of plants from all angles. Plant growth monitoring system based on big data came into being;

[0003] In the process of plant growth monitoring, the comprehensive growth situation of the area where the plants are located is a key indicator for determining abnormal plant growth status and an important basis for formulating reasonable maintenance strategies;

[0004] However, the existing plant growth monitoring methods mostly analyze indicators locally and perform independent control, which makes it impossible to determine and analyze the plant growth status from multiple levels, and it is even more difficult to optimize and adjust the early warning of abnormal plant growth status, which increases the cost of plant maintenance.

[0005] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0006] In order to solve the technical problems raised by the above background technology, the present invention is proposed. An embodiment of the present invention provides a plant growth monitoring system based on big data.

[0007] The purpose of the present invention can be achieved through the following technical scheme: a plant growth monitoring system based on big data is connected to a server, and the server has a data acquisition module, an appearance observation module, an annular state analysis module, an internal state measurement module and a comprehensive state warning module.

[0008] The appearance observation module analyzes the plant appearance growth status of the plant appearance information, obtains the plant overall growth state value, and sends it to the comprehensive state warning module;

[0009] The ring state analysis module performs plant environment state determination analysis on the plant environment information, obtains the total ring state estimation of the plant, and sends it to the comprehensive state warning module. The specific analysis is as follows:

[0010] The temperature and air humidity of the plant environment are obtained by the temperature sensor and humidity sensor respectively, the light intensity is obtained by the illuminometer, and the wavelength of light is obtained by the spectrometer. The absolute value of the difference between the light intensity and the set light intensity, and the absolute value of the difference between the light wavelength and the set light intensity are weighted to obtain the light intensity quality value, and the soil state value in the plant environment information is obtained and normalized and analyzed. According to the formula, the total ring value HZG of the plant is calculated;

[0011] The internal state analysis module analyzes and determines the internal state of the plant through the plant internal content index information, obtains the total internal state value of the plant, and sends it to the comprehensive state early warning module.

[0012] Furthermore, the specific operation steps of the total plant growth state value are as follows:

[0013] The total plant length value zzt is converted into length according to a certain ratio, and a cube is constructed with the length of the plant length value zzt as the side length of the cube; the proportion of plants with abnormal color growth B1 is converted into length according to a certain ratio, and a sphere is constructed inside the cube with the length of the proportion of plants with abnormal color growth B1 as the radius of the sphere, wherein the center of the sphere coincides with the center of the cube, and the solid volume formed by the cube and the sphere is identified, and marked as the total external growth value of the plant.

[0014] Furthermore, the specific operation steps of the total plant growth value and the proportion of plants with abnormal color growth B1 are as follows:

[0015] Step 1: Install multiple sets of image sensors in the plant growth area, transmit the collected images to the appearance observation module, decompose the plant image into m color feature vectors, divide each color feature into a separate cluster and mark it as H = {H 1 , H 2 ,...,H m},in

[0016] Step 2: Calculate the Euclidean distance between all color feature points Ha and the color feature points in the image.

[0017] m is the number of the color feature point, and the point with the minimum Euclidean distance from the color feature point Ha is taken, marked as Hc, and the clusters Ha and Hc are merged into a new cluster, marked as Hac, and Ha and Hc are removed from cluster H. Similarly, each new cluster is obtained, and the overall cluster is marked as H new , H new ={H 1 , H 2 ,...,H m / 2};

[0018] Step 3: Calculate the new cluster H new The Euclidean distance between the Hi cluster and other cluster feature points, i1 and j1 are the feature points of cluster Hi and cluster Hj, and the minimum Euclidean distance between the feature points of cluster Hi and cluster Hj is obtained. Similarly, the minimum Euclidean distance between cluster Hi and other clusters is compared, and the cluster point with the closest Euclidean distance to cluster Hi is taken, marked as Hc, cluster Hi and cluster Hc are merged into a new cluster, and cluster Hi and cluster Hc are removed;

[0019] Step 4: Repeat step 3 continuously. When the number of clusters reaches the set number of clusters, terminate the operation and obtain the final cluster H. fin , H fin ={H 1 , H 2 ,...,H A}, A is the set number of clusters, and each cluster H is counted fin The average RGB value of the color feature points in the cluster H fin The average RGB value of the cluster H is compared with the set plant growth RGB value. fin If the average RGB value of the cluster does not belong to the set plant growth RGB value range, the cluster is considered an abnormal growth cluster, and the color feature points in the abnormal growth cluster are mapped to the corresponding plants, and the ratio of plants with abnormal color features to the total number of plants in the area where the plants are located is counted, marked as B1;

[0020] The appearance observation module adjusts the contrast and brightness of the collected plant images to make the plants clearly distinguishable from the background, uses median filtering for denoising, binarizes the edge images of the plant images, extracts the contours of the plants, obtains the contours of the plants at each monitoring time, obtains the center point of the contours of the plants, and takes the distances from each contour point of the plant to the center line, and sums them up and marks them as l. n , n is the number of the monitoring time, n = 1, 2, 3...N, N is a positive integer, the difference between the moment of the monitoring time point and the moment of the previous monitoring time point is obtained, and marked as the time interval j of the monitoring time point n , calculate the wheel width state value lt of the plant according to the formula, obtain the contour surface area of ​​the plant at each monitoring time, calculate according to the formula, obtain the wheel surface state value lm of the plant, calculate the wheel width state value lt and the wheel surface state value lm of the plant by formula, obtain the growth state value st of the plant, obtain the set growth state value thresholds TT0, TT1 and TT2 of the plant, compare and analyze the growth state value of the plant with the threshold, obtain the number of abnormally curled plants, poorly growing plants, generally growing plants and well growing plants in the area where the plant is located, substitute them into the set formula model, and obtain the total length state value zzt of the plant.

[0021] Furthermore, the specific operation steps of the soil state value are as follows:

[0022] The soil porosity, soil aggregation stability value, soil organic matter content and soil pH value in the plant environment information are obtained, and the soil state value tz is calculated according to the formula.

[0023] Furthermore, the specific operation steps of the total internal state value of the plant are as follows:

[0024] Set the gradient reference intervals Q1, Q2, and Q3 of the plant's internal finger-shaped values, and substitute the internal finger-shaped values ​​of each plant into the preset gradient reference intervals Q1, Q2, and Q3 for comparative analysis;

[0025] When the inner finger value of the plant is within the preset gradient reference interval Q1, the plant enterprise is classified into the first-level inner plant set A1; when the inner finger value of the plant is within the preset gradient reference interval Q2, the corresponding plant is classified into the second-level inner plant set A2; when the inner finger value of the plant is within the preset gradient reference interval Q3, the corresponding plant is classified into the third-level inner plant set A3;

[0026] The number of plants in the first-level internal state plant set A1 and the third-level internal state plant set A3 in the area where the plants are located is obtained, and the number of the first-level internal state plant set is divided by the number of the third-level internal state plant set, and multiplied by the correction factor to obtain the total internal state value of the plants.

[0027] Furthermore, the specific operation steps of the plant intradigital value are as follows:

[0028] The chlorophyll content in the plant was obtained by a chlorophyll meter, and the content of plant nutrient elements was obtained by atomic absorption spectroscopy. The nutrient elements were specifically phosphorus, nitrogen and potassium. The sugar accumulation in the plant was determined by a sugar analyzer. The monitoring time was used as the horizontal axis, and the chlorophyll content, plant nutrient element content and sugar accumulation were used as the vertical axis. The chlorophyll content, plant nutrient element content and sugar accumulation change curves were established respectively. According to the formula, the chlorophyll fluctuation value ybz, the nutrient element fluctuation value yyb and the sugar accumulation fluctuation value tjb were obtained. The respective content reference standard straight lines were established on the chlorophyll content, plant nutrient element content and sugar accumulation change curves, and the midpoint between each change monitoring time and the adjacent previous monitoring time was taken. , the distance from the standard straight line, when the midpoint of the monitoring time is above the standard straight line, the distance from the standard straight line is corresponding to the first distance, when the midpoint of the monitoring time is below the standard straight line, the distance from the standard straight line is corresponding to the second distance, and the chlorophyll content, plant nutrient element content and sugar accumulation change curves of each monitoring time point curve are processed by tangent, and the slope of the tangent is obtained. When the slope is greater than zero, the slope is corresponding to the positive growth slope, and the statistics are summed. When the slope is less than zero, the slope is corresponding to the negative growth slope, and the statistics are summed. The formulas are calculated and analyzed to obtain the chlorophyll state value Z1, nutrient element state value Z2, sugar accumulation state value Z3 and plant internal finger value Z4 respectively.

[0029] Furthermore, the server connection also has:

[0030] The data acquisition module is used to collect plant appearance information, plant environment information and plant internal content index information, and send it to the appearance observation module, the ring state analysis module and the internal state measurement module;

[0031] The comprehensive warning module determines the overall state of the plant based on the total external growth state value of the plant, the total ring value of the plant and the total internal state value of the plant, and obtains corresponding warnings and treatments for plants in different states.

[0032] Furthermore, the specific operation steps for corresponding warnings and treatments for plants in different states are as follows:

[0033] The total external growth state value of the plant, the total ring value of the plant and the total internal state value of the plant are processed according to the graph construction to obtain the total state value of the plant;

[0034] A gradient reference threshold value Yu1 of the plant's total state value is set. When the plant's total state value is less than the preset gradient reference threshold value Yu1, a plant's total state abnormality signal is generated, an alarm is issued, and a description is displayed on a display screen set in the area where the plant is located in the form of a label-text description;

[0035] When the plant total state value is equal to the preset gradient reference threshold value Yu1, a general signal of the plant total state is generated, an alarm is issued, and a description is displayed on a display screen set in the area where the plant is located in the form of a description of the text words of label two;

[0036] When the plant overall state value is greater than the preset gradient reference threshold value Yu1, a plant overall state abnormality signal is generated, an alarm is issued, and a description is displayed on a display screen set in the area where the plant is located in the form of a description of the three text words of the label.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. The present invention analyzes plant appearance images to obtain the ratio of plants with abnormal color growth to the total number of plants, the total plant growth state value and the total external growth state value of the plants, and analyzes the soil state value, light intensity quality value, temperature and air humidity in the plant environmental information to obtain the total ring value of the plant. It can consider the color changes and appearance contour changes of the plants under the comprehensive growth situation of the area where the plants are located, and can also consider the problems of soil, light, temperature and humidity in the plant environment, thereby improving the accuracy of plant growth monitoring.

[0039] 2. The present invention determines and analyzes the internal content indicators of plants to obtain the plant internal finger value and the plant total internal state value, and obtains the plant total state value by analyzing the plant total state value, and analyzes the plant total state value gradient reference threshold to obtain corresponding early warning and treatment corresponding to plants in different states. It can determine and analyze the plant growth state based on the internal content of the plant, and can also optimize and adjust the early warning of abnormal plant growth state, thereby reducing the cost expenditure of plant maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. The following drawings are not intentionally scaled to the actual size, and the focus is on illustrating the main purpose of the present invention.

[0041] Figure 1 is a system block diagram of the present invention;

[0042] Figure 2 This is a curve chart showing changes in chlorophyll content, plant nutrient element content and sugar accumulation. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work also fall within the scope of protection of the present invention.

[0044] like Figure 1 - Figure 2 As shown, the plant growth monitoring system based on big data includes a server, and the server is communicatively connected with a data acquisition module, an appearance observation module, an annular state analysis module, an internal state measurement module and a comprehensive state warning module.

[0045] The data acquisition module is used to collect plant appearance information, plant environment information and plant internal content index information, and send it to the appearance observation module, the ring state analysis module and the internal state measurement module;

[0046] The appearance observation module is used to receive plant appearance information, analyze the plant appearance growth status based on it, and obtain the total plant growth state value. The specific analysis is as follows:

[0047] Through color feature analysis, the specific analysis of the proportion of plants with abnormal color growth is as follows:

[0048] Step 1: Install multiple sets of image sensors in the plant growth area, transmit the collected images to the appearance observation module, and decompose the plant image into m color feature vectors, marked as Each color feature is divided into a separate cluster and labeled H = {H 1 , H 2 ,...,H m},in

[0049] Step 2: Calculate the Euclidean distance between all color feature points Ha and the color feature points in the image.

[0050] m is the number of the color feature point, and the point with the minimum Euclidean distance from the color feature point Ha is taken, marked as Hc, and the clusters Ha and Hc are merged into a new cluster, marked as Hac, and Ha and Hc are removed from cluster H. Similarly, each new cluster is obtained, and the overall cluster is marked as H new , H new ={H 1 , H 2 ,...,H m / 2}, it should be noted that the new cluster contains two color feature vectors;

[0051] Step 3: Calculate the new cluster H new The Euclidean distance between the Hi cluster and other cluster feature points, i1 and j1 are the feature points of cluster Hi and cluster Hj, and the minimum Euclidean distance between the feature points of cluster Hi and cluster Hj is obtained. Similarly, the minimum Euclidean distance between cluster Hi and other clusters is compared, and the cluster point with the closest Euclidean distance to cluster Hi is taken, marked as Hc, cluster Hi and cluster Hc are merged into a new cluster, and cluster Hi and cluster Hc are removed;

[0052] Step 4: Repeat step 3 continuously. When the number of clusters reaches the set number of clusters, terminate the operation and obtain the final cluster H. fin , H fin ={H 1 , H 2 ,...,H A}, A is the set number of clusters, and each cluster H is counted fin The average RGB value of the color feature points will be clustered into H fin The average RGB value of the cluster H is compared with the set plant growth RGB value. fin If the cluster average RGB value does not belong to the set plant growth RGB value range, the cluster is an abnormal growth cluster, and the color feature points in the abnormal growth cluster are mapped to the corresponding plants, and the ratio of plants with abnormal color features to the total number of plants in the area where the plants are located is counted, marked as B1. It should be noted that the value can be R (95-145), G (150-205), B (45-95). Plants with darker and brighter colors than the standard are identified by color RGB, and there are different pathological conditions;

[0053] The appearance observation module adjusts the contrast and brightness of the collected plant images to make the plants clearly distinguishable from the background, uses median filtering for denoising, binarizes the edge images of the plant images, extracts the contours of the plants, obtains the contours of the plants at each monitoring time, obtains the center point of the contours of the plants, and takes the distances from each contour point of the plant to the center line, and sums them up and marks them as l. n , n is the number of the monitoring time, n = 1, 2, 3...N, N is a positive integer, the difference between the moment of the monitoring time point and the moment of the previous monitoring time point is obtained, and marked as the time interval j of the monitoring time point n , according to the formula Get the plant's wheelbase state value lt, and obtain the contour surface area of ​​the plant at each monitoring time, marked as S n , according to the formula The wheel state value lm of the plant is obtained, and the wheel state value lt of the plant and the wheel state value lm of the plant are normalized and substituted into the set formula st=f1*lt+f2*lm to obtain the growth state value st of the plant, wherein f1 and f2 are the set weight factors of the wheel state value of the plant and the wheel state value of the plant respectively. The professional field personnel confirm the values ​​and obtain the set plant growth state value thresholds TT0, TT1 and TT2. When the plant growth state value is less than the set growth state value threshold TT0, the plant corresponds to a curled abnormal growth plant. When the plant growth state value is less than the set growth state value threshold TT0, the plant corresponds to a curled abnormal growth plant. When the value of the plant's growth state value is less than the set growth state value threshold TT1 and greater than the set growth state value threshold TT0, the plant corresponds to a poor growth plant. When the value of the plant's growth state value is greater than the set growth state value threshold TT1 and less than the set growth state value threshold TT2, the plant corresponds to a normal growth plant. When the value of the plant's growth state value is greater than the set growth state value threshold TT2, the plant corresponds to a good growth plant. The number of abnormally curled growth plants, poor growth plants, normal growth plants and good growth plants in the area where the plant is located is obtained, marked as jy, bs, ys and lh respectively, and substituted into the set formula model.

[0054] The total plant growth state value zzt is obtained, where tot1, tot2, tot3 and tot4 are the set weight factors for the number of curled abnormal growth plants, poor growth plants, normal growth plants and good growth plants, respectively; λ is the set correction factor used to improve the accuracy of the calculation; e is a natural constant with a value of 2.718;

[0055] The total plant length value zzt is converted into a length according to a certain ratio, and a cube is constructed with the length of the plant length value zzt as the side length of the cube; the proportion of plants with abnormal color growth B1 is converted into a length according to a certain ratio, and a sphere is constructed inside the cube with the length of the proportion of plants with abnormal color growth B1 as the radius of the sphere, wherein the center of the sphere coincides with the center of the cube, and the solid volume formed by the cube and the sphere is identified, and marked as the total plant length value;

[0056] The ring state analysis module is used to receive plant environmental information and perform plant environmental status determination and analysis based on it to obtain the total ring state estimation of the plant. The specific analysis is as follows:

[0057] The soil state value, light intensity value, temperature and air humidity in the plant environment information are obtained and calibrated as tz, gz, wd and ks respectively, and then normalized and analyzed according to the formula The total ring estimate HZG of the plant is obtained, where μ is the preset correction factor and μ is taken as 0.276, h1, h2, h3 and h4 are weight factor coefficients of soil state value, light intensity quality value, temperature and air humidity, and h1, h2, h3 and h4 are all natural numbers greater than 0. The weight factor coefficient is used to balance the weight of each data in the formula calculation, thereby promoting the accuracy of the calculation result, and T1 and T2 are the set reference temperature and air humidity respectively;

[0058] It should be noted that the light intensity quality value refers to the absolute value of the difference between the light intensity and the set light intensity, and the absolute value of the difference between the light wavelength and the set light intensity. The two are weighted and multiplied by the corresponding proportional factor. The light intensity is obtained by the illuminance meter, and the light wavelength is obtained by the spectrometer; the temperature and air humidity of the plant environment are obtained by the temperature sensor and the humidity sensor respectively;

[0059] It should also be pointed out that the solution process of the soil state value is as follows:

[0060] The soil porosity, soil aggregation stability, soil organic matter content and soil pH value in the plant environment information are obtained and calibrated as tx, tj, yj and sj respectively, and then normalized and analyzed according to the formula The soil state value tz is calculated, where wos1, wos2, wos3 and wos4 are the preset weight factors of soil porosity, soil aggregation stability value, soil organic matter content and soil pH, σ is the preset correction factor, PH1 is the preset reference soil pH value, and P1 is the preset reference soil porosity;

[0061] It should be noted that soil porosity is the ratio of void area to total observation area observed and analyzed through image acquisition microscope; soil aggregation stability is the number of aggregates with particle size larger than the set particle size value observed and analyzed through image acquisition microscope; soil organic matter content is obtained through gas chromatograph analysis, and organic matter specifically includes humus, plant residues and microbial residues; soil pH value is obtained through pH meter;

[0062] The internal state analysis module is used to receive the internal content index information of the plant, analyze and determine the internal state of the plant based on it, and obtain the total internal state value of the plant. The specific analysis is as follows:

[0063] The chlorophyll content in the plant was obtained by a chlorophyll meter, and the content of plant nutrients was obtained by atomic absorption spectroscopy. The nutrients were phosphorus, nitrogen and potassium. The sugar accumulation in the plant was determined by a sugar analyzer. The chlorophyll content, plant nutrient content and sugar accumulation were taken as the vertical coordinates, and the chlorophyll content, plant nutrient content and sugar accumulation change curves were established, respectively, and marked as ylx , yt x and tj x , x represents the number of the monitoring time, x = 1, 2, 3...X, the value of X is a positive integer, according to the set formula The chlorophyll fluctuation value ybz, the nutrient element fluctuation value yyb and the sugar accumulation fluctuation value tjb are obtained, where y1, y2 and y3 are the set adjustment factors, X1, X2 and X3 are the average values ​​of chlorophyll content, plant nutrient element content and sugar accumulation, respectively. The reference standard straight lines of their respective contents are established on the chlorophyll content, plant nutrient element content and sugar accumulation change curves, see for details. Figure 2 , and take the distance from the standard straight line at the midpoint of each change monitoring time and the adjacent previous monitoring time. When the midpoint of the monitoring time is above the standard straight line, the distance from the standard straight line is corresponding to the first distance and marked as yy x 、ty x and jy x When the midpoint of the monitoring time is below the standard straight line, the distance from the standard straight line is corresponding to the second distance and marked as ye x 、te x and x , and perform tangent processing on the curves of each monitoring time point of the chlorophyll content, plant nutrient element content and sugar accumulation change curves, and obtain the slope of the tangent. When the slope is greater than zero, the slope corresponds to the positive growth slope, and the statistics are summed and marked as yzx, tzx and jzx respectively. When the slope is less than zero, the slope corresponds to the negative growth slope, and the statistics are summed and marked as yfx, tfx and jfx respectively, and they are normalized and substituted into the set formula group

[0064] The chlorophyll state value Z1, the nutrient element state value Z2, the sugar accumulation state value Z3 and the plant internal finger value Z4 are obtained respectively, among which y4, y5, y6, y7, y8, y9, y10, y11, y12, y13, y14, y15, y16, y17, y18, y19, y20 and y21 are the set weight factors respectively, and the specific values ​​are determined by professionals in the field;

[0065] Set the gradient reference intervals Q1, Q2, and Q3 of the plant's internal finger-shaped values, and substitute the internal finger-shaped values ​​of each plant into the preset gradient reference intervals Q1, Q2, and Q3 for comparative analysis, wherein the interval values ​​of Q1, Q2, and Q3 decrease in a gradient manner;

[0066] When the inner finger value of the plant is within the preset gradient reference interval Q1, the plant enterprise is classified into the first-level inner plant set A1; when the inner finger value of the plant is within the preset gradient reference interval Q2, the corresponding plant is classified into the second-level inner plant set A2; when the inner finger value of the plant is within the preset gradient reference interval Q3, the corresponding plant is classified into the third-level inner plant set A3;

[0067] Obtain the number of plants in the first-level internal state plant set A1 and the third-level internal state plant set A3 in the area where the plant is located, and divide the number of the first-level internal state plant set by the number of the third-level internal state plant set, and multiply by the correction factor to obtain the total internal state value of the plant;

[0068] The comprehensive state warning module is used to receive the total external growth state value of the plant, the total ring value of the plant and the total internal state value of the plant, and judge the overall state of the plant to obtain the corresponding warning and treatment for plants in different states. The specific analysis is as follows:

[0069] The total external growth state value of the plant and the total ring value of the plant are converted into lengths according to a certain ratio, and a rectangle is constructed with the lengths of the total external growth state value of the plant and the total ring value of the plant as the short side and the long side of the rectangle; the total internal state value of the plant is converted into lengths according to a certain ratio, and a circle is constructed with the length of the total internal state value of the plant as the radius of the circle, wherein the center of the circle coincides with the upper left vertex of the rectangle, and the figure formed by the circle and the rectangle is identified, and the formed outer perimeter is identified and marked as the total state value of the plant;

[0070] Set a gradient reference threshold value Yu1 of the plant total state value, and compare and analyze the plant total state value with the preset gradient reference threshold value Yu1;

[0071] When the plant's total state value is less than the preset gradient reference threshold value Yu1, a plant's total state abnormality signal is generated, an alarm is sounded, and a description is displayed on the display screen set in the plant's area in the form of a description of "the total state of the plant's area is abnormal, apply fertilizers, spray pesticides and strengthen sunlight management as soon as possible";

[0072] When the plant overall state value is equal to the preset gradient reference threshold value Yu1, a general signal of the plant overall state is generated, an alarm is issued, and a description is displayed on the display screen set in the plant area in the form of a description of the text words "the overall state of the plant area is general, strengthen monitoring and management";

[0073] When the plant's total state value is greater than the preset gradient reference threshold value Yu1, a plant's total state abnormality signal is generated, an alarm is sounded, and a description is displayed on the display screen set in the plant's area in the form of a description of "the total state of the plant's area is abnormal, apply fertilizers, spray pesticides and strengthen sunlight management as soon as possible";

[0074] The above is an explanation of the present invention and should not be considered as a limitation thereof. Although several exemplary embodiments of the present invention have been described, it will be readily appreciated by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present invention. Therefore, all such modifications are intended to be included within the scope of the present invention as defined in the claims. It should be understood that the above is an explanation of the present invention and should not be considered as being limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present invention is defined by the claims and their equivalents.

Claims

1. A plant growth monitoring system based on big data, comprising a server, characterized in that: Server connections are: The appearance observation module analyzes the plant appearance growth status of the plant, obtains the plant's total external growth value, and sends it to the comprehensive early warning module; The ring state analysis module determines and analyzes the plant environment information, obtains the total ring state estimation of the plant, and sends it to the comprehensive state warning module. The specific analysis is as follows: The temperature and air humidity of the plant environment are obtained by the temperature sensor and humidity sensor respectively, the light intensity is obtained by the illuminometer, and the wavelength of light is obtained by the spectrometer. The absolute value of the difference between the light intensity and the set light intensity, and the absolute value of the difference between the light wavelength and the set light intensity are weighted to obtain the light intensity quality value, and the soil state value in the plant environment information is obtained and normalized and analyzed. According to the formula, the total ring value HZG of the plant is calculated; The internal state analysis module analyzes and determines the internal state of the plant through the plant's internal content index information, obtains the total internal state value of the plant, and sends it to the comprehensive state warning module.

2. The plant growth monitoring system based on big data according to claim 1, characterized in that: The specific operation steps of the total plant growth state value are as follows: The total plant length value zzt is converted into length according to a certain ratio, and a cube is constructed with the length of the plant length value zzt as the side length of the cube; the proportion of plants with abnormal color growth B1 is converted into length according to a certain ratio, and a sphere is constructed inside the cube with the length of the proportion of plants with abnormal color growth B1 as the radius of the sphere, wherein the center of the sphere coincides with the center of the cube, and the solid volume formed by the cube and the sphere is identified, and marked as the total external growth value of the plant.

3. The plant growth monitoring system based on big data according to claim 2, characterized in that: The specific operation steps of the total plant growth value and the proportion of plants with abnormal color growth B1 are as follows: Step 1: Install multiple sets of image sensors in the plant growth area, transmit the collected images to the appearance observation module, decompose the plant image into m color feature vectors, divide each color feature into a separate cluster and mark it as H = {H1, H2, ..., H m },in Step 2: Calculate the Euclidean distance between all color feature points Ha and the color feature points in the image. m is the number of the color feature point, and the point with the minimum Euclidean distance from the color feature point Ha is taken, marked as Hc, and the clusters Ha and Hc are merged into a new cluster, marked as Hac, and Ha and Hc are removed from cluster H. Similarly, each new cluster is obtained, and the overall cluster is marked as H new , H new ={H1, H2, ..., H m / 2 }; Step 3: Calculate the new cluster H new The Euclidean distance between the Hi cluster and other cluster feature points, i1 and j1 are the feature points of cluster Hi and cluster Hj, and the minimum Euclidean distance between the feature points of cluster Hi and cluster Hj is obtained. Similarly, the minimum Euclidean distance between cluster Hi and other clusters is compared, and the cluster point with the closest Euclidean distance to cluster Hi is taken, marked as Hc, cluster Hi and cluster Hc are merged into a new cluster, and cluster Hi and cluster Hc are removed; Step 4: Repeat step 3 continuously. When the number of clusters reaches the set number of clusters, terminate the operation and obtain the final cluster H. fin , H fin ={H1, H2, ..., H A }, A is the set number of clusters, and each cluster H is counted fin The average RGB value of the color feature points in the cluster H fin The average RGB value of the cluster H is compared with the set plant growth RGB value. fin If the average RGB value of the cluster does not belong to the set plant growth RGB value range, the cluster is considered an abnormal growth cluster, and the color feature points in the abnormal growth cluster are mapped to the corresponding plants, and the ratio of plants with abnormal color features to the total number of plants in the area where the plants are located is counted, marked as B1; The appearance observation module adjusts the contrast and brightness of the collected plant images to make the plants clearly distinguishable from the background, uses median filtering for denoising, binarizes the edge images of the plant images, extracts the contours of the plants, obtains the contours of the plants at each monitoring time, obtains the center point of the contours of the plants, and takes the distances from each contour point of the plant to the center line, and sums them up and marks them as l. n , n is the number of the monitoring time, n = 1, 2, 3...N, N is a positive integer, the difference between the moment of the monitoring time point and the moment of the previous monitoring time point is obtained, and marked as the time interval j of the monitoring time point n , calculate the wheel width state value lt of the plant according to the formula, obtain the contour surface area of ​​the plant at each monitoring time, calculate according to the formula, obtain the wheel surface state value lm of the plant, calculate the wheel width state value lt and the wheel surface state value lm of the plant by formula, obtain the growth state value st of the plant, obtain the set growth state value thresholds TT0, TT1 and TT2 of the plant, compare and analyze the growth state value of the plant with the threshold, obtain the number of abnormally curled plants, poorly growing plants, generally growing plants and well growing plants in the area where the plant is located, substitute them into the set formula model, and obtain the total length state value zzt of the plant.

4. The plant growth monitoring system based on big data according to claim 1, characterized in that: The specific operation steps of the soil state value are as follows: The soil porosity, soil aggregation stability value, soil organic matter content and soil pH value in the plant environment information are obtained, and the soil state value tz is calculated according to the formula.

5. The plant growth monitoring system based on big data according to claim 1, characterized in that: The specific operation steps of the total internal state value of the plant are as follows: Set the gradient reference intervals Q1, Q2, and Q3 of the plant's internal finger-shaped values, and substitute the internal finger-shaped values ​​of each plant into the preset gradient reference intervals Q1, Q2, and Q3 for comparative analysis; When the inner finger value of the plant is within the preset gradient reference interval Q1, the plant enterprise is classified into the first-level inner plant set A1; when the inner finger value of the plant is within the preset gradient reference interval Q2, the corresponding plant is classified into the second-level inner plant set A2; when the inner finger value of the plant is within the preset gradient reference interval Q3, the corresponding plant is classified into the third-level inner plant set A3; The number of plants in the first-level internal state plant set A1 and the third-level internal state plant set A3 in the area where the plants are located is obtained, and the number of the first-level internal state plant set is divided by the number of the third-level internal state plant set, and multiplied by the correction factor to obtain the total internal state value of the plants.

6. The plant growth monitoring system based on big data according to claim 5, characterized in that: The specific operation steps of the plant intradigital value are as follows: The chlorophyll content in the plant was obtained by a chlorophyll meter, and the content of plant nutrient elements was obtained by atomic absorption spectroscopy. The nutrient elements were specifically phosphorus, nitrogen and potassium. The sugar accumulation in the plant was determined by a sugar analyzer. The monitoring time was used as the horizontal axis, and the chlorophyll content, plant nutrient element content and sugar accumulation were used as the vertical axis. The chlorophyll content, plant nutrient element content and sugar accumulation change curves were established respectively. According to the formula, the chlorophyll fluctuation value ybz, the nutrient element fluctuation value yyb and the sugar accumulation fluctuation value tjb were obtained. The respective content reference standard straight lines were established on the chlorophyll content, plant nutrient element content and sugar accumulation change curves, and the midpoint between each change monitoring time and the adjacent previous monitoring time was taken. , the distance from the standard straight line, when the midpoint of the monitoring time is above the standard straight line, the distance from the standard straight line is corresponding to the first distance, when the midpoint of the monitoring time is below the standard straight line, the distance from the standard straight line is corresponding to the second distance, and the chlorophyll content, plant nutrient element content and sugar accumulation change curves of each monitoring time point curve are processed by tangent, and the slope of the tangent is obtained. When the slope is greater than zero, the slope is corresponding to the positive growth slope, and the statistics are summed. When the slope is less than zero, the slope is corresponding to the negative growth slope, and the statistics are summed. The formulas are calculated and analyzed to obtain the chlorophyll state value Z1, nutrient element state value Z2, sugar accumulation state value Z3 and plant internal finger value Z4 respectively.

7. The plant growth monitoring system based on big data according to claim 1, characterized in that: The server is also connected to: The data acquisition module is used to collect plant appearance information, plant environment information and plant internal content index information, and send it to the appearance observation module, the ring state analysis module and the internal state measurement module; The comprehensive warning module determines the overall state of the plant based on the total external growth state value of the plant, the total ring value of the plant and the total internal state value of the plant, and obtains corresponding warnings and treatments for plants in different states.

8. The plant growth monitoring system based on big data according to claim 7, characterized in that: The specific steps for warning and handling of plants in different states are as follows: The total external growth state value of the plant, the total ring value of the plant and the total internal state value of the plant are processed according to the graph construction to obtain the total state value of the plant; A gradient reference threshold value Yu1 of the plant's total state value is set. When the plant's total state value is less than the preset gradient reference threshold value Yu1, a plant's total state abnormality signal is generated, an alarm is issued, and a description is displayed on a display screen set in the area where the plant is located in the form of a label-text description; When the plant's total state value is equal to the preset gradient reference threshold value Yu1, a general signal of the plant's total state is generated, an alarm is sounded, and a description is displayed on a display screen set in the area where the plant is located in the form of a description of the text words of label two; When the plant total state value is greater than the preset gradient reference threshold value Yu1, a plant total state abnormal signal is generated, an alarm is issued, and a description is displayed on a display screen set in the area where the plant is located in the form of a description of the three text words of the label.

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