Crop growth state intelligent evaluation system and method based on agricultural big data

Abstract

The invention discloses an agricultural big data-based crop growth state intelligent evaluation system and method, and the method comprises the steps: collecting historical growth environment data, and generating a mapping relation between a climate abnormal fluctuation data type and a soil abnormal fluctuation data type corresponding to an influence growth abnormal state data type; obtaining a significant climate environment factor and a significant soil environment factor; and collecting values of the significant climate environment factors and the significant soil environment factors corresponding to different growth state data in different growth stages, calculating growth state coefficients of the crops, and evaluating the growth states of the crops. The evaluation system comprises a climate environment monitoring module, a soil environment monitoring module, a growth data acquisition module and a big data server. According to the invention, growth problems of crops in different growth stages can be found in time, corresponding measures can be taken conveniently, and the real-time performance of agricultural production is improved.

Description

Technical Field

[0001] The present invention relates to the field of agricultural big data, and in particular to a crop growth status intelligent evaluation system and method based on agricultural big data. Background Art

[0002] With the development of smart agriculture and the agricultural market, agricultural big data is becoming more and more important in the agricultural planting process. With the continuous development of science and technology, smart agriculture has gradually become an important tool for modern agriculture, and even replaces artificial crops for management. Smart agriculture combines sensors, the Internet of Things and artificial intelligence technologies to monitor and collect environmental parameters of farmland (such as temperature, humidity, light intensity, etc.) in real time, and conducts data analysis and decision-making through algorithms. However, the existing development of smart agriculture still has problems such as insufficient accuracy and weak real-time performance in the assessment of crop growth status, which cannot meet the actual needs of modern agricultural production. At the same time, in the current agricultural production process, the assessment of crop growth status is mostly still in the manual stage, and the growth status of crops in different growth stages is judged by experience, which will have strong subjectivity, is not applicable to large-scale crop planting, and has high labor costs. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides a system and method for intelligently evaluating crop growth status based on agricultural big data.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: Provided is a method for intelligently evaluating crop growth status based on agricultural big data, comprising: Step S1: Based on the historical growth environment data collected during the historical crop growth process in the study area, the historical growth environment data includes climate environment data and soil environment data, screening the soil abnormal fluctuation data and climate abnormal fluctuation data of crops at different growth stages, and generating a mapping relationship between the climate abnormal fluctuation data type and the soil abnormal fluctuation data type and the data type affecting the abnormal growth state; Step S2: Obtain the mapping relationship corresponding to each stage of the crop, and calculate the influence weight of different data types on the crop growth status in the growth cycle, obtain significant climate environmental factors and significant soil environmental factors, and establish a significant soil environmental factor data group and a significant climate environmental factor data group; Step S3: During the growth process of the crop, the values ​​of significant climate environmental factors and significant soil environmental factors corresponding to different growth status data at different growth stages are collected, the growth status coefficient of the crop is calculated, and the growth status of the crop is evaluated.

[0005] Further, step S1 includes: Step S11: Establish a climate environment data set that affects crop growth based on historical crop growth environment data collected during the historical crop growth process in the study area. and soil environmental data set , n is the amount of climate and environmental data, m is the number of soil environmental data, For the n Climate and environmental data, For the m Soil environment data; Step S12: According to different growth stages of the crop growth process T 1- T 2. Collect historical growth status data and establish corresponding crop growth status data group ; Step S13: Calculate the crop growth stage T 1- T 2 Fluctuation values ​​of climate and environmental data: ; in, For growth stage T 1- T 2 between n Fluctuation value of climate and environmental data, For the moment T 1st collection n Climate and environmental data, For the moment T 2nd collection n Climate and environmental data; Step S14: Setting the fluctuation threshold of climate environment data ; like , then determine the n The climate and environment data are abnormal climate fluctuation data; if , then determine the n The climate and environmental data are the normal climate fluctuation data; Step S15: Calculate the crop growth stage T 1- T Fluctuation value of soil environmental data within 2: ; in, For growth stage T 1- T 2 between m Soil environment data fluctuation value, For the moment T 1st collection mSoil environment data, For the moment T 2nd collection m Soil environment data; Step S16: Setting the fluctuation threshold of soil environment data ; like , then determine the m The soil environment data are soil abnormal fluctuation data; if , then determine the m The soil environment data are the normal soil fluctuation data; Step S17: Filter out the climate environment data group and soil environmental data set All abnormal climate fluctuation data and abnormal soil fluctuation data in the and soil abnormal fluctuation data set ;in, is the amount of data in the climate anomaly fluctuation data set, is the amount of data in the soil abnormal fluctuation data group; Step S18: Calculate the crop growth stage T 1- T 2 corresponds to the fluctuation value of crop growth status data; ; in, For the u Crop growth status data in the growth stage T 1- T Fluctuation value between 2, For the moment T 2nd collection u Crop growth status data, For the moment T 1st collection u Crop growth status data; Step S19: Setting the crop growth status data fluctuation threshold ; like , then determine the u The crop growth status data is abnormal fluctuation data. , then determine the u The crop growth status data are normal fluctuation data; Step S110: Filter out all abnormal fluctuation data in the crop growth status data group to form a crop growth status abnormal fluctuation data group , The amount of data in the abnormal fluctuation data group of crop growth status; Step S111: generating a mapping relationship between the climate abnormal fluctuation data type and the soil abnormal fluctuation data type and the data type affecting the abnormal state of growth; ; in, For the The data type of climate abnormal fluctuation data, For the The data type of soil abnormal fluctuation data is For the The data type of abnormal fluctuation data of crop growth status.

[0006] Further, step S2 includes: Step S21: Execute steps S11-S111 according to the historical growth environment data and historical growth status data of different growth stages collected during the crop growth cycle, obtain the mapping relationship corresponding to each growth stage, and calculate the influence weight of different data types on the crop growth status during the growth cycle; ; ; in, For data types For data types The influence weight of For data types For data types The influence weight of Represents data type With data types There is a mapping relationship between them. Represents data type With data types There is a mapping relationship between them. A For data types With data types The number of successful mappings in all growth stages of the crop, B For data types With data types The number of successful mappings in all growth stages of the crop, V is the number of growth stages divided into the crop growth cycle, N 1 is the data type The number of times it appears in the climate anomaly fluctuation data set, N 2 is the data type The number of occurrences in the soil abnormal fluctuation data set, A , B The initial value of is 0; Step S22: Setting the weight threshold of the impact of the data type of abnormal climate fluctuation data and the data type of abnormal soil fluctuation data on the data type of crop growth status data , ; like , then determine the data type Is the type of data that affects crop growth Otherwise, it is not a significant climate and environmental factor; like , then determine the data type Is the type of data that affects crop growth Otherwise, it is not a significant soil environmental factor. Step S23: Count the significant climate and environmental factors corresponding to the data type of each growth state data during the crop growth process, and generate a data set of significant climate and environmental factors affecting the corresponding growth state data of the crop. , For the E Significant climate and environmental factors, E is the number of significant climate and environmental factors; Count the significant soil environmental factors corresponding to the data type of each growth status data during the crop growth process, and generate a data set of significant soil environmental factors that affect the corresponding growth status data of the crop , For the W Significant soil environmental factors W is the number of significant soil environmental factors.

[0007] Further, step S3 includes: Step S31: During the crop growth process, the values ​​of significant climate environmental factors and significant soil environmental factors corresponding to different growth status data at different growth stages are collected to calculate the crop growth status coefficient K ; Step S32: Setting the reference value of the crop growth state coefficient ; like , then the crop growth status is good. , it is judged that the growth state of the crop is poor.

[0008] Furthermore, the growth state coefficient K The calculation method is: ; in, i is the number of the growth status data, For the i The ideal value of growth status data,e is the number of the significant climate and environmental factors, w is the number of significant soil environmental factors, a e For the e The values ​​of significant climate and environmental factors, b w For the w The values ​​of significant soil environmental factors, h e For the e The ideal value of a significant soil environmental factor, t w For the w The ideal value of a significant soil environmental factor.

[0009] A crop growth status intelligent evaluation system based on agricultural big data is provided, which executes the above-mentioned crop growth status intelligent evaluation method based on agricultural big data, and comprises: A climate and environment monitoring module, including a plurality of climate sensors, for monitoring a plurality of climate and environment data during the growth process of crops; A soil environment monitoring module, including a plurality of soil sensors, for monitoring a plurality of soil environment data during the growth of crops; Growth data collection module, used to collect growth status data during crop growth; The big data server includes a data analysis module and a data storage module. The data storage module is used to store the collected climate environment data and soil environment data. The data analysis module is used to analyze the collected climate environment data, soil environment data and growth status data and evaluate the growth status of crops.

[0010] The beneficial effects of the present invention are as follows: by collecting soil environment data and climate environment data historically, combined with the growth status data of crops at different growth stages, the influence analysis of soil environment data and climate environment data on growth status data is realized, the factors affecting growth status data are screened, and it is convenient to conduct intelligent evaluation of growth status through soil environment data and climate environment data in the future, and a crop status evaluation system with the help of big data analysis technology is built, which is suitable for growth status evaluation in different growth cycles, different soil environments and climate environments, and provides a scientific decision-making basis for agricultural production. It can timely discover growth problems in different growth stages of crops, facilitate the adoption of corresponding measures, and improve the real-time performance of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a flow chart of the intelligent assessment method of crop growth status based on agricultural big data. DETAILED DESCRIPTION

[0012] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0013] like Figure 1 As shown, a method for intelligently evaluating crop growth status based on agricultural big data includes: Step S1: Based on the historical growth environment data collected during the historical crop growth process in the study area, the historical growth environment data includes climate environment data and soil environment data, screen the abnormal soil fluctuation data and abnormal climate fluctuation data of crops at different growth stages, and generate a mapping relationship between the abnormal climate fluctuation data type and the abnormal soil fluctuation data type and the data type that affects the abnormal growth state.

[0014] Step S1 specifically includes: Step S11: Establish a climate environment data set that affects crop growth based on historical crop growth environment data collected during the historical crop growth process in the study area. and soil environmental data set , n is the amount of climate and environmental data, m is the number of soil environmental data, For the n Climate and environmental data, For the m soil environment data; in this embodiment, the climate environment data includes temperature, light, air humidity, etc., and the soil environment data includes soil moisture, nutrient concentration, soil viscosity, etc.

[0015] Step S12: According to different growth stages of the crop growth process T 1- T 2. Collect historical growth status data and establish corresponding crop growth status data group ; Step S13: Calculate the crop growth stage T 1- T 2 Fluctuation values ​​of climate and environmental data: ; in, For growth stage T 1- T 2 between n Fluctuation value of climate and environmental data, For the moment T 1st collectionn Climate and environmental data, For the moment T 2nd collection n Climate and environmental data; Step S14: Setting the fluctuation threshold of climate environment data , which indicates the allowable value of fluctuation of climate and environmental data; like , then determine the n The climate and environment data are abnormal climate fluctuation data; if , then determine the n The climate and environmental data are the normal climate fluctuation data; Step S15: Calculate the crop growth stage T 1- T Fluctuation value of soil environmental data within 2: ; in, For growth stage T 1- T 2 between m Soil environment data fluctuation value, For the moment T 1st collection m Soil environment data, For the moment T 2nd collection m Soil environment data; Step S16: Setting the fluctuation threshold of soil environment data , which indicates the allowable value of fluctuation of climate and environmental data; like , then determine the m The soil environment data are soil abnormal fluctuation data; if , then determine the m The soil environment data are the normal soil fluctuation data; During the growth of crops, the requirements for the climate and soil environment vary with the different growth stages, and abnormal fluctuations in the climate and soil environment will affect the growth of crops. In this embodiment, the sowing period, germination period, reproductive period and maturity period of crops can be used as different growth stages.

[0016] Step S17: Filter out the climate environment data group and soil environmental data set All abnormal climate fluctuation data and abnormal soil fluctuation data in the and soil abnormal fluctuation data set ;in, is the amount of data in the climate anomaly fluctuation data set, is the amount of data in the soil abnormal fluctuation data group; Step S18: Calculate the crop growth stage T 1- T 2 corresponds to the fluctuation value of crop growth status data; ; in, For the u Crop growth status data in the growth stage T 1- T Fluctuation value between 2, For the moment T 2nd collection u Crop growth status data, For the moment T 1st collection u Crop growth status data; in this embodiment, the crop growth status data includes height, leaf area, leaf chlorophyll content, etc.

[0017] Step S19: Setting the crop growth status data fluctuation threshold , which indicates the allowable value of fluctuation of crop growth status data; like , then determine the u The crop growth status data is abnormal fluctuation data. , then determine the u The crop growth status data are normal fluctuation data; Step S110: Filter out all abnormal fluctuation data in the crop growth status data group to form a crop growth status abnormal fluctuation data group , The amount of data in the abnormal fluctuation data group of crop growth status; Step S111: generating a mapping relationship between the climate abnormal fluctuation data type and the soil abnormal fluctuation data type and the data type affecting the abnormal state of growth; ; in, For the The data type of climate abnormal fluctuation data, For the The data type of soil abnormal fluctuation data is For the The data type of abnormal fluctuation data of crop growth status.

[0018] Step S2: Obtain the mapping relationship corresponding to each stage of the crop, and calculate the influence weight of different data types on the crop growth status in the growth cycle, obtain significant climate and environmental factors, significant soil environmental factors, and establish significant soil environmental factor data groups and significant climate and environmental factor data groups.

[0019] Step S2 specifically includes: Step S21: Execute steps S11-S111 according to the historical growth environment data and historical growth status data of different growth stages collected during the crop growth cycle, obtain the mapping relationship corresponding to each growth stage, and calculate the influence weight of different data types on the crop growth status during the growth cycle; ; ; in, For data types For data types The influence weight of For data types For data types The influence weight of Represents data type With data types There is a mapping relationship between them. Represents data type With data types There is a mapping relationship between them. A For data types With data types The number of successful mappings in all growth stages of the crop, B For data types With data types The number of successful mappings in all growth stages of the crop, V is the number of growth stages divided into the crop growth cycle, N 1 is the data type The number of times it appears in the climate anomaly fluctuation data set, N 2 is the data type The number of occurrences in the soil abnormal fluctuation data set, A , B The initial value of is 0; Step S22: Setting the weight threshold of the impact of the data type of abnormal climate fluctuation data and the data type of abnormal soil fluctuation data on the data type of crop growth status data , ; like , then determine the data type Is the type of data that affects crop growth Otherwise, it is not a significant climate and environmental factor; like , then determine the data type Is the type of data that affects crop growth Otherwise, it is not a significant soil environmental factor. Step S23: Count the significant climate and environmental factors corresponding to the data type of each growth state data during the crop growth process, and generate a data set of significant climate and environmental factors affecting the corresponding growth state data of the crop. , For the E Significant climate and environmental factors, E is the number of significant climate and environmental factors; Count the significant soil environmental factors corresponding to the data type of each growth status data during the crop growth process, and generate a data set of significant soil environmental factors that affect the corresponding growth status data of the crop , For the W Significant soil environmental factors W is the number of significant soil environmental factors.

[0020] Step S3: During the growth process of the crop, the values ​​of significant climate environmental factors and significant soil environmental factors corresponding to different growth status data at different growth stages are collected, the growth status coefficient of the crop is calculated, and the growth status of the crop is evaluated.

[0021] Step S3 specifically includes: Step S31: During the crop growth process, the values ​​of significant climate environmental factors and significant soil environmental factors corresponding to different growth status data at different growth stages are collected to calculate the crop growth status coefficient K ; Step S32: Setting the reference value of the crop growth state coefficient ; like , then the crop growth status is good. , it is judged that the growth state of the crop is poor.

[0022] Furthermore, the growth state coefficient K The calculation method is: ; in, i is the number of the growth status data, For the i The ideal value of growth status data, e is the number of the significant climate and environmental factors, wis the number of significant soil environmental factors, a e For the e The values ​​of significant climate and environmental factors, b w For the w The values ​​of significant soil environmental factors, h e For the e The ideal value of a significant soil environmental factor, t w For the w The ideal value of a significant soil environmental factor.

[0023] A crop growth status intelligent assessment system based on agricultural big data, which implements the above-mentioned crop growth status intelligent assessment method based on agricultural big data, comprises: A climate and environment monitoring module, including a plurality of climate sensors, for monitoring a plurality of climate and environment data during the growth process of crops; A soil environment monitoring module, including a plurality of soil sensors, for monitoring a plurality of soil environment data during the growth of crops; Growth data collection module, used to collect growth status data during crop growth; The big data server includes a data analysis module and a data storage module. The data storage module is used to store the collected climate environment data and soil environment data. The data analysis module is used to analyze the collected climate environment data, soil environment data and growth status data and evaluate the growth status of crops.

[0024] The present invention collects soil environment data and climate environment data historically, combines the growth status data of crops at different growth stages, realizes the influence analysis of soil environment data and climate environment data on growth status data, screens the influencing factors on growth status data, facilitates the subsequent intelligent evaluation of growth status through soil environment data and climate environment data, builds a crop status evaluation system with the help of big data analysis technology, adapts to the growth status evaluation of different growth cycles, different soil environments and climate environment requirements, and provides a scientific decision-making basis for agricultural production. It can timely discover growth problems in different growth stages of crops, facilitate the adoption of corresponding measures, and improve the real-time performance of agricultural production.

Claims

1. A method for intelligent assessment of crop growth status based on agricultural big data, characterized in that: include: Step S1: Based on the historical growth environment data collected during the historical crop growth process in the study area, the historical growth environment data includes climate environment data and soil environment data, screening the soil abnormal fluctuation data and climate abnormal fluctuation data of crops at different growth stages, and generating a mapping relationship between the climate abnormal fluctuation data type and the soil abnormal fluctuation data type and the data type affecting the abnormal growth state; Step S2: Obtain the mapping relationship corresponding to each stage of the crop, and calculate the influence weight of different data types on the crop growth status in the growth cycle, obtain significant climate environmental factors and significant soil environmental factors, and establish a significant soil environmental factor data group and a significant climate environmental factor data group; Step S3: During the growth process of the crop, the values ​​of significant climate environmental factors and significant soil environmental factors corresponding to different growth status data at different growth stages are collected, the growth status coefficient of the crop is calculated, and the growth status of the crop is evaluated.

2. The method for intelligent assessment of crop growth status based on agricultural big data according to claim 1, characterized in that: The step S1 comprises: Step S11: Establish a climate environment data set that affects crop growth based on historical crop growth environment data collected during the historical crop growth process in the study area. and soil environmental data set , n is the amount of climate and environmental data, m is the number of soil environmental data, For the n Climate and environmental data, For the m Soil environment data; Step S12: According to different growth stages of the crop growth process T 1- T 2. Collect historical growth status data and establish corresponding crop growth status data group ; Step S13: Calculate the crop growth stage T 1- T 2 Fluctuation values ​​of climate and environmental data: ； in, For growth stage T 1- T 2 between n Fluctuation value of climate and environmental data, For the moment T 1st collection n Climate and environmental data, For the moment T 2nd collection n Climate and environmental data; Step S14: Setting the fluctuation threshold of climate environment data ; like , then determine the n The climate and environment data are abnormal climate fluctuation data; if , then determine the n The climate and environmental data are the normal climate fluctuation data; Step S15: Calculate the crop growth stage T 1- T Fluctuation value of soil environmental data within 2: ； in, For growth stage T 1- T 2 between m Soil environment data fluctuation value, For the moment T 1st collection m Soil environment data, For the moment T 2nd collection m Soil environment data; Step S16: Setting the fluctuation threshold of soil environment data ; like , then determine the m The soil environment data are soil abnormal fluctuation data; if , then determine the m The soil environment data are the normal soil fluctuation data; Step S17: Filter out the climate environment data group and soil environmental data set All abnormal climate fluctuation data and abnormal soil fluctuation data in the and soil abnormal fluctuation data set ;in, is the amount of data in the climate anomaly fluctuation data set, is the amount of data in the soil abnormal fluctuation data group; Step S18: Calculate the crop growth stage T 1- T 2 corresponds to the fluctuation value of crop growth status data; ； in, For the u Crop growth status data in the growth stage T 1- T Fluctuation value between 2, For the moment T 2nd collection u Crop growth status data, For the moment T 1st collection u Crop growth status data; Step S19: Setting the crop growth status data fluctuation threshold ; like , then determine the u The crop growth status data is abnormal fluctuation data. , then determine the u The crop growth status data are normal fluctuation data; Step S110: Filter out all abnormal fluctuation data in the crop growth status data group to form a crop growth status abnormal fluctuation data group , The amount of data in the abnormal fluctuation data group of crop growth status; Step S111: generating a mapping relationship between the climate abnormal fluctuation data type and the soil abnormal fluctuation data type and the data type affecting the abnormal state of growth; ； in, For the The data type of climate abnormal fluctuation data, For the The data type of soil abnormal fluctuation data is For the The data type of abnormal fluctuation data of crop growth status.

3. The method for intelligent assessment of crop growth status based on agricultural big data according to claim 2 is characterized in that: The step S2 comprises: Step S21: Execute steps S11-S111 according to the historical growth environment data and historical growth status data of different growth stages collected during the crop growth cycle, obtain the mapping relationship corresponding to each growth stage, and calculate the influence weight of different data types on the crop growth status during the growth cycle; ； ； in, For data types For data types The influence weight of For data types For data types The influence weight of Represents data type With data types There is a mapping relationship between them. Represents data type With data types There is a mapping relationship between them. A For data types With data types The number of successful mappings in all growth stages of the crop, B For data types With data types The number of successful mappings in all growth stages of the crop, V is the number of growth stages divided into the crop growth cycle, N 1 is the data type The number of times it appears in the climate anomaly fluctuation data set, N 2 is the data type The number of occurrences in the soil abnormal fluctuation data set, A , B The initial value of is 0; Step S22: Setting the weight threshold of the impact of the data type of abnormal climate fluctuation data and the data type of abnormal soil fluctuation data on the data type of crop growth status data , ; like , then determine the data type Is the type of data that affects crop growth Otherwise, it is not a significant climate and environmental factor; like , then determine the data type Is the type of data that affects crop growth Otherwise, it is not a significant soil environmental factor. Step S23: Count the significant climate and environmental factors corresponding to the data type of each growth state data during the crop growth process, and generate a data set of significant climate and environmental factors affecting the corresponding growth state data of the crop. , For the E Significant climate and environmental factors, E is the number of significant climate and environmental factors; Count the significant soil environmental factors corresponding to the data type of each growth status data during the crop growth process, and generate a data set of significant soil environmental factors that affect the corresponding growth status data of the crop , For the W Significant soil environmental factors, W is the number of significant soil environmental factors.

4. The method for intelligent assessment of crop growth status based on agricultural big data according to claim 3 is characterized in that: The step S3 comprises: Step S31: During the crop growth process, the values ​​of significant climate environmental factors and significant soil environmental factors corresponding to different growth status data at different growth stages are collected to calculate the crop growth status coefficient K ; Step S32: Setting the reference value of the crop growth state coefficient ; like , then the crop growth status is good. , it is judged that the growth state of the crop is poor.

5. The method for intelligent assessment of crop growth status based on agricultural big data according to claim 2, characterized in that: The growth state coefficient K The calculation method is: ； in, i is the number of the growth status data, For the i The ideal value of growth status data, e is the number of the significant climate and environmental factors, w is the number of significant soil environmental factors, a e For the e The values ​​of significant climate and environmental factors, b w For the w The values ​​of significant soil environmental factors, h e For the e The ideal value of a significant soil environmental factor, t w For the w The ideal value of a significant soil environmental factor.

6. A crop growth status intelligent assessment system based on agricultural big data, executing the crop growth status intelligent assessment method based on agricultural big data according to any one of claims 1 to 5, characterized in that: include: A climate and environment monitoring module, including a plurality of climate sensors, for monitoring a plurality of climate and environment data during the growth process of crops; A soil environment monitoring module, including a plurality of soil sensors, for monitoring a plurality of soil environment data during the growth of crops; Growth data collection module, used to collect growth status data during crop growth; The big data server includes a data analysis module and a data storage module. The data storage module is used to store the collected climate environment data and soil environment data. The data analysis module is used to analyze the collected climate environment data, soil environment data and growth status data and evaluate the growth status of crops.

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