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CN119179978A - Agricultural planting monitoring method and device based on big data - Google Patents

Agricultural planting monitoring method and device based on big data Download PDF

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CN119179978A
CN119179978A CN202411219552.1A CN202411219552A CN119179978A CN 119179978 A CN119179978 A CN 119179978A CN 202411219552 A CN202411219552 A CN 202411219552A CN 119179978 A CN119179978 A CN 119179978A
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CN119179978B (en
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蒲强
汪海鹰
罗莹
易发胜
杨晨辉
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Chengdu University
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Abstract

The invention provides a large data-based agricultural planting monitoring method and device, and relates to the technical field of agricultural planting monitoring.A large data storage platform stores image data, soil data and water source data of an agricultural planting area, and features of crops, soil features and water source features are obtained by extracting features of the image data, the soil data and the water source data; the method comprises the steps of analyzing crop characteristics to generate crop state coefficients and determining crop states according to the crop state coefficients, establishing a database containing standard data of each state, calculating standard soil characteristics and standard water source characteristics according to the database, scoring soil and water sources according to differences between the soil characteristics and the water source characteristics and the corresponding standard characteristics, generating environment assessment indexes according to soil and water source grading results, comparing the environment assessment indexes with preset thresholds, and classifying planting conditions into good, normal and risk according to comparison results.

Description

Agricultural planting monitoring method and device based on big data
Technical Field
The invention relates to the technical field of agricultural planting monitoring, in particular to an agricultural planting monitoring method and device based on big data.
Background
As the global population continues to grow, the demand for food and agricultural products increases dramatically, and the challenges facing agricultural production are also increasing. Modern agriculture not only needs to increase the yield, but also needs to cope with multiple adverse factors such as limited land resources, aggravation of environmental pollution, climate change and the like. Among them, the decrease of soil fertility, the non-sustainable cultivation mode, the excessive use of chemical fertilizers and pesticides, and the increasing lack of water resources, result in the gradual deterioration of soil quality and water resources, seriously affecting the growth and yield of crops. In addition, extreme weather, such as drought, flooding, and high temperatures, from climate change increases uncertainty and risk of agricultural production.
Traditional agricultural management methods rely mainly on farmers' experience or local data to make decisions, such as soil composition analysis or simple meteorological data acquisition. The mode of experience driving and single data source is difficult to deal with complex and changeable environmental conditions in modern agriculture, and especially under the conditions of large farmland and obvious regional climate difference, the growth state of crops cannot be accurately monitored and estimated. In addition, existing agricultural monitoring technologies generally focus on factors in only one aspect, and lack comprehensive analysis of the overall view and multiple dimensions of the agricultural ecosystem. For example, crop growth is only assessed by soil analysis or climate data, other key parameters such as crop coverage rate, crop health status, soil moisture, nutrients, disease conditions and the like are ignored, the method leads to inaccuracy of agricultural production decisions, environmental changes and risks cannot be effectively dealt with in time, and finally agricultural production efficiency and product quality are affected.
Based on this, a solution that integrates multidimensional data and can accurately monitor and predict the growth state and environmental condition of crops is needed in modern agriculture, so as to improve the scientificity and intellectualization level of agricultural management, reduce resource waste, improve the yield and quality of crops, and promote sustainable development of agriculture.
In the prior art, publication number CN117993705B discloses a smart agriculture planting monitoring system and method based on big data, and the smart agriculture planting monitoring system comprises an image acquisition module, a soil acquisition module, a weather acquisition module, a water source acquisition module, a big data analysis module, a database, an information processing module and an information sending module, wherein the image acquisition module is unmanned aerial vehicle image acquisition equipment and is used for acquiring crop planting real-time image information, the soil acquisition module is used for acquiring planting area soil component information, the weather acquisition module is used for acquiring weather prediction information, and the water source collection module is used for acquiring planting area water source quality information and planting area water source distance information. This prior art can be better carry out the farming monitoring, helps promoting the development of wisdom agriculture, realizes the intellectuality and the refinement of agricultural production, reduces time and the cost of manual intervention, more accurately obtains the crop condition of planting, carries out the accurate adjustment to crops. However, the prior art still has defects, the environmental conditions required by each state of plant growth are different, and if the identification of the plant state is wrong, the monitoring is lost, so that the actual demands of plants cannot be effectively met. The technical defects not only affect the accuracy of environmental regulation, but also can cause resource waste, yield reduction and deterioration of plant health.
The above data disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include data that does not form the prior art that is already known to those of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide a big data-based agricultural planting monitoring method and device, which are used for solving the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the agricultural planting monitoring method and device based on big data comprises the following specific steps:
step 1, collecting image data, soil data and water source data of an agricultural planting area, wherein the image data comprises a planting area remote sensing image, the soil data comprises N content, P content, K content, organic matter content, soil PH value and compactness, and the water source data comprises dissolved oxygen, water source PH and heavy metal content;
Step 2, storing image data, soil data and water source data into a big data storage platform, and carrying out feature extraction on the image data, the soil data and the water source data to obtain crop features, wherein the crop features comprise crop coverage rate and crop color coefficients, the soil features comprise soil nutrient indexes and soil growth inhibition indexes, and the water source features are probiotics indexes;
Step 3, analyzing the characteristics of crops to generate crop state coefficients, determining the growing period of the crops by comparing the crop state coefficients with a preset threshold value, wherein the growing period comprises a juvenile period, a growing period and a mature period, and acquiring standard soil data and standard water source data corresponding to the growing period;
calculating standard soil characteristics and standard water source characteristics, and grading the soil and the water source respectively according to the difference value between the soil characteristics and the standard soil characteristics and the difference value between the water source characteristics and the standard water source characteristics;
and 5, comprehensively analyzing the soil and water source grading results to generate an environment evaluation index, comparing the environment evaluation index with a preset threshold value, and classifying planting conditions into good, normal and risk according to the comparison result.
Further, the specific logic based on the extraction of the characteristics of the crops is that graying treatment is carried out on the remote sensing image of the planting area, the inter-class variance of each threshold value in the gray histogram is calculated, the optimal separation threshold value is selected through the inter-class variance, the remote sensing image of the planting area is subjected to threshold segmentation by using the optimal separation threshold value, the remote sensing image of the planting area is divided into a crop part and other parts, the ratio of pixel points of the crop part to pixel points of the remote sensing image of the total area is calculated to be used as the crop coverage rate, the average gray value of the crop part is calculated to obtain the crop color coefficient, and the specific logic based on the acquisition of the crop coverage rate is as follows:
Wherein G is crop coverage rate, Q is the number of partial pixels of crops, and M is the number of pixels of the remote sensing image of the total area;
The specific logic on which the crop color coefficient is obtained is as follows:
wherein Cl is a crop color coefficient, and H i is a gray value of an ith pixel point of the crop part.
Further, carrying out dimensionalization treatment on the N content, the P content, the K content and the organic matter content to generate a soil nutrient index, carrying out mathematical analysis on the pH value and the compactness of the soil to generate a soil growth inhibition index, wherein the specific formula for generating the soil nutrient index is as follows:
Wherein Ns is soil nutrient index, N is N content, P is P content, K is K content, and CH is organic content;
the specific formula for generating the soil resistance index is as follows:
wherein Hz is soil resistance index, PH is soil PH value, and sigma is compactness.
Further, the specific logic based on the water source characteristic extraction is that the probiotics index is generated by the dissolved oxygen amount, the water source PH and the heavy metal content, and the specific formula based on the generation is as follows:
Wherein, gow is a probiotics index, C O is dissolved oxygen, PH w is water source PH, Z is heavy metal content.
Further, the specific logic on which the crop states are divided is that crop features are analyzed to generate crop state coefficients, and the specific formulas on which the crop states are based are as follows:
Su=G*Cl
wherein Su is a crop state coefficient, G is a crop coverage rate, and Cl is a crop color coefficient;
A crop state threshold Su 0 is preset, the crop state being defined as a juvenile period when Su <0.2Su 0, the crop state being defined as a growing period when Su 0≤Su<0.7Su0 is 0.2 and the crop state being defined as a mature period when Su 0≤Su<Su0 is 0.7.
Further, the method comprises the steps of,
The concrete logic for scoring the soil and the water source is that standard soil characteristics are calculated based on standard soil data, the difference between the soil characteristics and the standard soil characteristics is calculated, soil scores are generated according to the difference between the soil characteristics and the standard soil characteristics, standard water source characteristics are calculated based on standard water source data, the difference between the water source characteristics and the standard water source characteristics is calculated, the water source scores are generated according to the difference between the water source characteristics and the standard water source characteristics, and the concrete formula for generating the soil scores is as follows:
Ss=|Ns-Ns0|+(Hz-Hz0)2
wherein Sco is soil score, ns is soil nutrient index, ns 0 is standard soil nutrient index, hz is soil growth inhibition index, hz 0 is standard soil growth inhibition index;
the specific formula according to which the water source score is generated is as follows:
where Sw is the water source score, gow is the probiotic index, and Gow 0 is the standard probiotic index.
Further, comprehensively analyzing the soil and water source rating results to generate an environment assessment index, wherein the specific formula on which the environment assessment index is generated is as follows:
Wherein Sz is an environmental assessment index, sco is soil scoring, sw is water source scoring;
Setting an environment evaluation threshold Sz 0, defining the crop planting condition as risk when Sz is less than 0.3Sz 0, defining the crop planting condition as normal when 0.2Sz 0≤Sz<0.8Sz0, and defining the crop planting condition as good when 0.8Sz 0≤Sz<Sz0.
The invention further provides a big data-based agricultural planting monitoring device, which is used for any step of the big data-based agricultural planting monitoring method, and specifically comprises the following steps:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring image data, soil data and water source data of an agricultural planting area, the image data comprise a planting area remote sensing image, the soil data comprise N content, P content, K content, organic matter content, soil PH value and compactness, and the water source data comprise dissolved oxygen, water source PH and heavy metal content;
The characteristic extraction module is used for storing the image data, the soil data and the water source data into the big data storage platform, and extracting characteristics of the image data, the soil data and the water source data to obtain crop characteristics, wherein the crop characteristics comprise crop coverage rate and crop color coefficients, the soil characteristics comprise soil nutrient indexes and soil anti-growth indexes, and the water source characteristics are probiotics indexes;
The state determining module is used for generating crop state coefficients through analysis of crop characteristics, determining the growing period of crops in a mode of comparing the crop state coefficients with a preset threshold value, wherein the growing period comprises a juvenile period, a growing period and a mature period, and acquiring standard soil data and standard water source data corresponding to the growing period;
the data scoring module is used for calculating standard soil characteristics and standard water source characteristics, and scoring the soil and the water source respectively according to the difference value between the soil characteristics and the standard soil characteristics and the difference value between the water source characteristics and the standard water source characteristics;
The state classification module is used for comprehensively analyzing the soil and water source rating results, generating an environment assessment index, comparing the environment assessment index with a preset threshold value, and classifying planting conditions into good, normal and risk according to the comparison result. Further, the method comprises the steps of.
Compared with the prior art, the invention has the beneficial effects that:
The invention generates a crop state coefficient capable of quantifying the growth time of crops through image information which can be acquired in real time, and divides the crops into a juvenile period, a growing period and a mature period through threshold processing of the crop state coefficient, thereby not only accurately identifying the growth period of the crops, but also providing corresponding monitoring according to different stages, further improving the yield and quality of the crops, optimizing the resource utilization rate and promoting the intelligent and fine management of agricultural production.
Drawings
FIG. 1 is a schematic flow chart of the whole method of the invention.
FIG. 2 is a schematic diagram of the overall device structure of the present invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "up", "down", "left", "right" and the like are used only to indicate a relative positional relationship, and when the absolute position of the object to be described is changed, the relative positional relationship may be changed accordingly.
Examples:
Referring to fig. 1, the present invention provides a technical solution:
The agricultural planting monitoring method based on big data comprises the following specific steps:
step 1, collecting image data, soil data and water source data of an agricultural planting area, wherein the image data comprises a planting area remote sensing image, the soil data comprises N content, P content, K content, organic matter content, soil PH value and compactness, and the water source data comprises dissolved oxygen, water source PH and heavy metal content;
the remote sensing image of the planting area is shot through the unmanned aerial vehicle, and the soil data and the water source data are directly obtained through the sensor. The N content, the P content and the K content are respectively nitrogen content, phosphorus content and potassium fertilizer content.
Step 2, storing image data, soil data and water source data into a big data storage platform, and carrying out feature extraction on the image data, the soil data and the water source data to obtain crop features, wherein the crop features comprise crop coverage rate and crop color coefficients, the soil features comprise soil nutrient indexes and soil growth inhibition indexes, and the water source features are probiotics indexes;
The specific logic for extracting the characteristics of crops is that gray processing is carried out on the remote sensing image of a planting area, the inter-class variance of each threshold value in a gray histogram is calculated, the optimal separation threshold value is selected through the inter-class variance, the remote sensing image of the planting area is subjected to threshold segmentation by using the optimal separation threshold value, the remote sensing image of the planting area is divided into a crop part and other parts, the ratio of pixel points of the crop part to pixel points of the total remote sensing image of the total area is calculated to be used as the coverage rate of crops, the average gray value of the crop part is calculated, and the specific logic for calculating the optimal separation threshold value is as follows:
Traversing all possible thresholds, dividing a remote sensing image of a planting area into other parts and crop parts according to the numerical value of each threshold, counting the number of pixels of the other parts and the crop parts, calculating the average value of gray values of the pixels of the other parts and the crop parts, and calculating the inter-class variance of the corresponding threshold according to the specific formula:
σ(t)2＝wB(t)*wF(t)*(μB(t)-μF(t))2
Wherein σ (t) 2 represents the inter-class variance at the threshold value of t, t e [0,255], and t e N +,wB(t)、wF (t) is the number of pixels of the other part and the crop part at the threshold value of t, respectively, and μ B(t)、μF (t) is the average of the gray values of the pixels of the other part and the crop part at the threshold value of t, respectively;
and calculating the value of the threshold value when the inter-class variance reaches the maximum, and defining the value as the optimal separation threshold value.
The specific logic on which the crop coverage rate is obtained is as follows:
Wherein G is crop coverage rate, Q is the number of partial pixels of crops, and M is the number of pixels of the remote sensing image of the total area;
The crop coverage rate G reflects the size of crops in a planting area, and the larger the crops are, the larger the occupied area is, and the higher the crop coverage rate is;
The specific logic on which the crop color coefficient is obtained is as follows:
wherein Cl is a crop color coefficient, and H i is a gray value of an ith pixel point of the crop part. The crop color coefficient Cl reflects the color shade of the crop, the greater the value of which, the darker the crop color.
Carrying out dimensionalization treatment on the N content, the P content, the K content and the organic matter content to generate a soil nutrient index, carrying out mathematical analysis on the pH value and the compactness of the soil to generate a soil growth inhibition index, wherein the specific formula for generating the soil nutrient index is as follows:
The soil nutrient index Ns reflects the condition of the nutrients contained in the soil, the larger the soil nutrient index Ns is, the more nutrients are contained in the soil, N, P, K and the organic matters are the most important nutrients required by the growth of crops, and the higher the content is, the higher the nutrient content in the soil is naturally, and the higher the soil nutrient index Ns is.
The specific formula for generating the soil resistance index is as follows:
wherein Hz is soil resistance index, PH is soil PH value, and sigma is compactness. The soil resistance index Hz reflects the influence of factors for preventing the growth of crops on the crops, and the larger the value is, the soil condition is not suitable for the growth of the crops.
The specific logic based on the water source extraction characteristics is that the probiotics index is generated by dissolved oxygen, water source PH and heavy metal content, and the specific formula based on the generation is as follows:
Wherein, gow is a probiotics index, C O is dissolved oxygen, PH w is water source PH, Z is heavy metal content. The probiotics index Gow reflects the extent to which the water source in the environment is suitable for the growth of crops, and the larger the value is, the more suitable the water source in the environment is for the growth of crops.
Step 3, analyzing the characteristics of crops to generate crop state coefficients, determining the growing period of the crops by comparing the crop state coefficients with a preset threshold value, wherein the growing period comprises a juvenile period, a growing period and a mature period, and acquiring standard soil data and standard water source data corresponding to the growing period;
The standard soil data and the standard water source data are determined by inviting agricultural specialists to carry out demonstration analysis on the soil data and the water source data in the corresponding period, and the description of the standard soil data and the standard water source data is omitted.
The specific logic for dividing the crop state is that crop characteristics are analyzed to generate crop state coefficients, and the specific formula is as follows:
Su=G*Cl
The crop state coefficient Su reflects the existence time of the crops, the larger the value of the crop state coefficient Su is, the longer the existence time of the crops is, and the generation of the coefficient can provide an important basis for judging the growth stage of the crops. The crop color coefficient Cl reflects the color depth of crops, the larger the value is, the darker the color of the crops is along with the growth of the existing time of the crops, the crop coverage rate G reflects the size of crops in a planting area, the larger the crop coverage rate G is, the larger the occupied area of the crops is, the longer the existing time of the crops is, and when the crop coverage rate G is reduced due to aging of the crops, the crops are harvested completely and do not need detection.
A crop state threshold Su 0 is preset, and when Su <0.2Su 0, the crop state is defined as a juvenile period, and when 0.2Su 0≤Su<0.7Su0, the crop state is defined as a growing period, and when 0.7Su 0≤Su<Su0, the crop state is defined as a mature period. The environmental conditions required for each growth stage of the crop are different, and the present embodiment facilitates monitoring of each stage by dividing the crop into a juvenile stage, a growing stage and a mature stage by the crop state threshold Su 0.
Calculating standard soil characteristics and standard water source characteristics, and grading the soil and the water source respectively according to the difference value between the soil characteristics and the standard soil characteristics and the difference value between the water source characteristics and the standard water source characteristics;
The concrete logic for scoring the soil and the water source is that standard soil characteristics are calculated based on standard soil data, the difference between the soil characteristics and the standard soil characteristics is calculated, soil scores are generated according to the difference between the soil characteristics and the standard soil characteristics, standard water source characteristics are calculated based on standard water source data, the difference between the water source characteristics and the standard water source characteristics is calculated, the water source scores are generated according to the difference between the water source characteristics and the standard water source characteristics, and the concrete formula for generating the soil scores is as follows:
Ss=|Ns-Ns0|+(Hz-Hz0)2
Wherein, sco is soil score, ns is soil nutrient index, ns 0 is standard soil nutrient index, hz is soil growth inhibition index, hz 0 is standard soil growth inhibition index, the soil score Sco reflects the comprehensive influence of soil condition on crop growth, the larger the value of the soil score Sco is, the more suitable the crop growth is.
The specific formula according to which the water source score is generated is as follows:
Where Sw is the water source score, gow is the benefit watering index, and Gow 0 is the standard benefit watering index. The water source score Sw reflects the comprehensive influence of the water source environment on the growth of crops, and the larger the water source environment is, the more suitable the water source environment is for the growth of crops.
And 5, comprehensively analyzing the soil and water source grading results to generate an environment evaluation index, comparing the environment evaluation index with a preset threshold value, and classifying planting conditions into good, normal and risk according to the comparison result.
Comprehensively analyzing the soil and water source rating results to generate an environment assessment index, wherein the specific formula for generating the environment assessment index is as follows:
The environment evaluation index Sz reflects the comprehensive influence of the environment condition on the growth state of crops, and the larger the value is, the more suitable the surrounding environment of the crops for the growth of the crops is.
Setting an environment evaluation threshold Sz 0, defining the crop planting condition as risk when Sz is less than 0.3Sz 0, defining the crop planting condition as normal when 0.2Sz 0≤Sz<0.8Sz0, and defining the crop planting condition as good when 0.8Sz 0≤Sz<Sz0. The method quantifies the influence of the environmental conditions around the crops on the growth of the crops through the environmental evaluation index Sz, and divides the growth environment of the crops into clear and visual good, normal and risk through a threshold comparison mode, thereby being beneficial to timely taking measures to protect and promote the growth of the crops. The method can provide more visual and clear information for the evaluation of the crop growth environment, and is beneficial to perfecting the management and regulation of the crop growth environment.
Referring to fig. 2, the present invention further provides a big data-based agricultural planting monitoring device, which is used for any step of the big data-based agricultural planting monitoring method, and specifically includes:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring image data, soil data and water source data of an agricultural planting area, the image data comprise a planting area remote sensing image, the soil data comprise N content, P content, K content, organic matter content, soil PH value and compactness, and the water source data comprise dissolved oxygen, water source PH and heavy metal content;
the characteristic extraction module is used for storing the image data, the soil data and the water source data into the big data storage platform, and extracting the characteristics of the image data, the soil data and the water source data to obtain plant characteristics, wherein the plant characteristics comprise plant coverage rate and plant color coefficient, the soil characteristics comprise soil nutrient index and soil growth inhibition index, and the water source characteristics are beneficial irrigation indexes;
The system comprises a state determining module, a data scoring module, a soil and water source grading module, a water source grading module and a water source grading module, wherein the state determining module is used for analyzing plant characteristics to generate plant state coefficients, determining the growth period of crops in a mode of comparing the plant state coefficients with a preset threshold value, and acquiring standard soil data and standard water source data corresponding to the growth period, wherein the growth period comprises a juvenile period, a growth period and a mature period;
The state classification module is used for comprehensively analyzing the soil and water source rating results, generating an environment evaluation index, comparing the environment evaluation index with a preset threshold value, and classifying planting conditions into good, normal and risk according to the comparison result. Further, the method comprises the steps of.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.

Claims (8)

1. The agricultural planting monitoring method based on big data is characterized by comprising the following specific steps:
step 1, collecting image data, soil data and water source data of an agricultural planting area, wherein the image data comprises a planting area remote sensing image, the soil data comprises N content, P content, K content, organic matter content, soil PH value and compactness, and the water source data comprises dissolved oxygen, water source PH and heavy metal content;
Step 2, storing image data, soil data and water source data into a big data storage platform, and carrying out feature extraction on the image data, the soil data and the water source data to obtain crop features, wherein the crop features comprise crop coverage rate and crop color coefficients, the soil features comprise soil nutrient indexes and soil growth inhibition indexes, and the water source features are probiotics indexes;
Step 3, analyzing the characteristics of crops to generate crop state coefficients, determining the growing period of the crops by comparing the crop state coefficients with a preset threshold value, wherein the growing period comprises a juvenile period, a growing period and a mature period, and acquiring standard soil data and standard water source data corresponding to the growing period;
calculating standard soil characteristics and standard water source characteristics, and grading the soil and the water source respectively according to the difference value between the soil characteristics and the standard soil characteristics and the difference value between the water source characteristics and the standard water source characteristics;
and 5, comprehensively analyzing the soil and water source grading results to generate an environment evaluation index, comparing the environment evaluation index with a preset threshold value, and classifying planting conditions into good, normal and risk according to the comparison result.
2. The agricultural planting monitoring method based on big data, which is characterized by comprising the specific logic of carrying out graying treatment on a planting area remote sensing image, calculating the inter-class variance of each threshold value in a gray level histogram, selecting an optimal separation threshold value through the inter-class variance, carrying out threshold segmentation on the planting area remote sensing image by using the optimal separation threshold value, dividing the planting area remote sensing image into a crop part and other parts, calculating the ratio of pixel points of the crop part to pixel points of the total area remote sensing image to serve as crop coverage rate, calculating the average gray level of the crop part to obtain a crop color coefficient, and obtaining the specific logic based on the crop coverage rate, wherein the specific logic comprises the following steps:
Wherein G is crop coverage rate, Q is the number of partial pixels of crops, and M is the number of pixels of the remote sensing image of the total area;
The specific logic on which the crop color coefficient is obtained is as follows:
wherein Cl is a crop color coefficient, and H i is a gray value of an ith pixel point of the crop part.
3. The agricultural planting monitoring method based on big data, which is characterized by comprising the following specific logic of extracting soil characteristics, namely carrying out dimensionalization treatment on N content, P content, K content and organic matter content to generate a soil nutrient index, carrying out mathematical analysis on the pH value and compactness of soil to generate a soil growth-inhibition index, and generating a specific formula according to the soil nutrient index:
Wherein Ns is soil nutrient index, N is N content, P is P content, K is K content, and CH is organic content;
the specific formula for generating the soil resistance index is as follows:
wherein Hz is soil resistance index, PH is soil PH value, and sigma is compactness.
4. The agricultural planting monitoring method based on big data, according to claim 1, is characterized in that the specific logic based on the water source characteristic extraction is that a probiotics index is generated through dissolved oxygen, water source PH and heavy metal content, and the specific formula based on the generation is as follows:
Wherein, gow is a probiotics index, C O is dissolved oxygen, PH w is water source PH, Z is heavy metal content.
5. The agricultural planting monitoring method based on big data according to claim 1, wherein the specific logic based on which the crop state is divided is that crop characteristics are analyzed to generate crop state coefficients, and the specific formula based on which is:
Su=G*Cl
wherein Su is a crop state coefficient, G is a crop coverage rate, and Cl is a crop color coefficient;
A crop state threshold Su 0 is preset, the crop state being defined as a juvenile period when Su <0.2Su 0, the crop state being defined as a growing period when Su 0≤Su<0.7Su0 is 0.2 and the crop state being defined as a mature period when Su 0≤Su<Su0 is 0.7.
6. The agricultural planting monitoring method based on big data, which is characterized by comprising the specific logic of calculating standard soil characteristics based on standard soil data, calculating the difference between the soil characteristics and the standard soil characteristics, generating soil scores according to the difference between the soil characteristics and the standard soil characteristics, calculating standard water source characteristics based on standard water source data, calculating the difference between the water source characteristics and the standard water source characteristics, generating water source scores according to the difference between the water source characteristics and the standard water source characteristics, and generating a specific formula based on the soil scores, wherein the specific formula comprises the following steps:
Ss=|Ns-Ns0|+(Hz-Hz0)2
wherein Sco is soil score, ns is soil nutrient index, ns 0 is standard soil nutrient index, hz is soil growth inhibition index, hz 0 is standard soil growth inhibition index;
the specific formula according to which the water source score is generated is as follows:
where Sw is the water source score, gow is the probiotic index, and Gow 0 is the standard probiotic index.
7. The agricultural planting monitoring method based on big data according to claim 1, wherein the soil and water source rating results are comprehensively analyzed to generate an environment assessment index, and the specific formula according to which the environment assessment index is generated is as follows:
Wherein Sz is an environmental assessment index, sco is soil scoring, sw is water source scoring;
An environmental assessment threshold Sz 0 is set, where Sz <0.3Sz 0 defines crop planting as a risk, 0.2Sz 0≤Sz<0.8Sz0 defines crop planting as normal, and 0.8Sz 0≤Sz<Sz0 defines crop planting as good.
8. The big data-based agricultural planting monitoring device is characterized by being used for realizing any step of the big data-based agricultural planting monitoring method as set forth in claims 1-7, and specifically comprising the following steps:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring image data, soil data and water source data of an agricultural planting area, the image data comprise a planting area remote sensing image, the soil data comprise N content, P content, K content, organic matter content, soil PH value and compactness, and the water source data comprise dissolved oxygen, water source PH and heavy metal content;
The characteristic extraction module is used for storing the image data, the soil data and the water source data into the big data storage platform, and extracting characteristics of the image data, the soil data and the water source data to obtain crop characteristics, wherein the crop characteristics comprise crop coverage rate and crop color coefficients, the soil characteristics comprise soil nutrient indexes and soil anti-growth indexes, and the water source characteristics are probiotics indexes;
The state determining module is used for generating crop state coefficients through analysis of crop characteristics, determining the growing period of crops in a mode of comparing the crop state coefficients with a preset threshold value, wherein the growing period comprises a juvenile period, a growing period and a mature period, and acquiring standard soil data and standard water source data corresponding to the growing period;
the data scoring module is used for calculating standard soil characteristics and standard water source characteristics, and scoring the soil and the water source respectively according to the difference value between the soil characteristics and the standard soil characteristics and the difference value between the water source characteristics and the standard water source characteristics;
The state classification module is used for comprehensively analyzing the soil and water source rating results, generating an environment assessment index, comparing the environment assessment index with a preset threshold value, and classifying planting conditions into good, normal and risk according to the comparison result.
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