Agricultural monitoring method and system based on five-base cooperative integration

Abstract

The invention provides an agricultural monitoring method and system based on five-base cooperative integration, and belongs to the field of agricultural monitoring, and the method comprises the steps: monitoring a target region based on a space-based satellite system, and determining a first region which is a region in the target region; monitoring the first area based on an air-based remote sensing system, and determining a second area; the second area is an area in the first area, and the first area is an agricultural area; monitoring an area meeting a first condition in the second area based on the aviation unmanned aerial vehicle monitoring system to obtain first monitoring data; monitoring an area meeting a second condition in the second area based on the mobile monitoring vehicle system to obtain second monitoring data; monitoring an area meeting a third condition in the second area based on a ground observation system to obtain third monitoring data; and obtaining an agricultural monitoring result based on the first monitoring data, the second monitoring data and the third monitoring data. The accuracy and reliability of agricultural monitoring can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of agricultural monitoring, and more specifically, to an agricultural monitoring method and system based on the coordinated integration of five bases. Background Art

[0002] With the development of modern agriculture, precision agriculture has become an important direction for improving agricultural production efficiency, ensuring the quality of agricultural products and achieving sustainable agricultural development. Agricultural production is affected by a variety of factors, including crop growth, soil fertility, meteorological conditions, and the occurrence of pests and diseases. Comprehensive, accurate and real-time monitoring of these factors is critical to optimizing agricultural management decisions.

[0003] Traditional agricultural monitoring methods mainly rely on manual field inspections and ground fixed-point observations. Although manual inspections can directly observe the growth status of crops, they are inefficient, making it difficult to cover large areas of farmland, and the accuracy of monitoring results is low.

[0004] It can be seen that an accurate and reliable agricultural monitoring method is needed. Summary of the invention

[0005] The purpose of the present invention is to provide an agricultural monitoring method and system based on the coordinated integration of five bases to improve the accuracy and reliability of agricultural monitoring.

[0006] In a first aspect of the disclosed embodiment, there is provided an agricultural monitoring method based on five-base coordinated integration, including: monitoring a target area based on a space-based satellite system to determine a first area, where the first area is an area in the target area; monitoring the first area based on an air-based remote sensing system to determine a second area, where the second area is an area in the first area, where the first area is an agricultural area; Based on the aerial drone monitoring system, the area in the second area that meets the first condition is monitored to obtain first monitoring data; based on the mobile monitoring vehicle system, the area in the second area that meets the second condition is monitored to obtain second monitoring data; based on the ground observation system, the area in the second area that meets the third condition is monitored to obtain third monitoring data; Agricultural monitoring results are obtained based on the first monitoring data, the second monitoring data and the third monitoring data.

[0007] According to a second aspect of the disclosed embodiment, there is provided an agricultural monitoring system based on the coordinated integration of five bases, including: a region determination module, for monitoring a target region based on a space-based satellite system, and determining a first region, where the first region is a region in the target region; for monitoring the first region based on an air-based remote sensing system, and determining a second region, where the second region is a region in the first region, where the first region is an agricultural region; The area monitoring module is used to monitor the areas in the second area that meet the first condition based on the aerial drone monitoring system to obtain the first monitoring data; monitor the areas in the second area that meet the second condition based on the mobile monitoring vehicle system to obtain the second monitoring data; monitor the areas in the second area that meet the third condition based on the ground observation system to obtain the third monitoring data. The result output module is used to obtain the agricultural monitoring result based on the first monitoring data, the second monitoring data, and the third monitoring data.

[0008] In the third aspect of the embodiments of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned agricultural monitoring method based on five-base collaborative integration are implemented.

[0009] In the fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the above-mentioned agricultural monitoring method based on five-base collaborative integration are implemented.

[0010] The beneficial effects of the agricultural monitoring method and system based on five-base collaborative integration provided by the embodiments of the present disclosure are as follows: Through five different levels of monitoring means, namely the space-based satellite system, the air-based remote sensing system, the aerial drone monitoring system, the mobile monitoring vehicle system, and the ground observation system, the present disclosure realizes the full-range and multi-level coverage of the agricultural area, can obtain more comprehensive information of the agricultural area, and improves the comprehensiveness and accuracy of agricultural monitoring. After initially determining the agricultural area through the space-based and air-based systems, the present disclosure selects suitable monitoring means for further monitoring according to different conditions, can more accurately locate the problem area, and improves the accuracy and reliability of agricultural monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0012] Figure 1 It is a flowchart of the agricultural monitoring method based on five-base collaborative integration provided by an embodiment of the present disclosure; Figure 2 It is a structural block diagram of the agricultural monitoring system based on five-base collaborative integration provided by an embodiment of the present disclosure; Figure 3 It is a schematic block diagram of the electronic device provided by an embodiment of the present disclosure. Detailed implementation manners

[0013] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present disclosure. However, those skilled in the art should clearly understand that the present disclosure can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present disclosure.

[0014] To make the objectives, technical solutions, and advantages of the present disclosure clearer, the following will be described through specific embodiments in conjunction with the accompanying drawings.

[0015] Please refer to Figure 1 , Figure 1 , which is a schematic flowchart of an agricultural monitoring method based on five-base collaborative integration provided by an embodiment of the present disclosure. The method includes: S101: Monitor a target area based on a space-based satellite system to determine a first area, where the first area is an area in the target area; monitor the first area based on an air-based remote sensing system to determine a second area; the second area is an area in the first area, and the first area is an agricultural area.

[0016] In this embodiment, the space-based satellite system refers to a system composed of various satellites operating in space and their equipped remote sensing and other monitoring devices. The space-based satellite system can observe large areas of the earth's surface and obtain various information such as vegetation indices, land cover types, and meteorological data. It has the characteristics of a wide coverage range, but the spatial resolution is relatively limited. It can be used to grasp the overall situation of a large geographical area from a macroscopic perspective and provide basic data for subsequent more detailed monitoring.

[0017] The target area is a specific geographical range that is preset for agricultural monitoring. This area can cover various agricultural lands such as farmlands, orchards, and tea gardens, as well as the surrounding natural environment areas related thereto.

[0018] The first area is a specific area selected based on certain monitoring indicators or characteristics after monitoring the target area by the space-based satellite system. For example, when the space-based satellite system monitors the Normalized Difference Vegetation Index (NDVI) of the target area, the sub-areas with abnormal NDVI values will be determined as the first area, which is part of the target area where plant growth anomalies and other situations may be initially judged to exist.

[0019] Monitoring the target area based on the space-based satellite system to determine the first area includes: Monitor the first index of the target area based on the space-based satellite system to obtain the first index monitoring result; Determine the first area based on the first index monitoring result.

[0020] The first index can be the NDVI index. Determining the first area based on the first index monitoring result includes: In response to the difference between the first index monitoring result of the target sub-area and the first index monitoring result of the historical target sub-area being greater than the first index threshold, the target sub-area is the first area; In response to the difference between the first index monitoring result of the target sub-area and the average first index monitoring result within the area at a first distance from the target sub-area being greater than the second index threshold, the target sub-area is the first area.

[0021] In this embodiment, the target sub-area is an area within the target area. When the target sub-area meets certain conditions, it will be determined as the first area. The determination of the first area can be compared with its own historical first index monitoring result. If the difference between the current NDVI index and the historical NDVI index is too large, that is, exceeding the first index threshold, it may indicate that something abnormal has occurred in this area and more detailed monitoring is required. It can also be based on the difference between the NDVI index of the target sub-area and the average of the NDVI indices of the surrounding areas. If the difference is too large, that is, exceeding the second index threshold, it may indicate poor plant growth or species invasion in this area. The first index threshold and the second index threshold can be determined based on experience.

[0022] An airborne remote sensing system is a remote sensing monitoring system built on a platform that can be located at a certain height in the air and relatively close to the ground. Compared with the space-based satellite system, it has a higher spatial resolution and can observe the specific features and detailed changes of objects on the ground more clearly and meticulously, and can conduct in-depth monitoring of relatively small areas.

[0023] The second area is, on the basis of the first area, to further monitor the agricultural characteristics within the first area through the airborne remote sensing system. The second area can be determined according to the following method: Monitor the first area based on the airborne remote sensing system to determine the second area, including: Monitor the agricultural characteristics of the first area based on the airborne remote sensing system to obtain the first agricultural monitoring result; Determine the second area based on the first agricultural monitoring result.

[0024] Agricultural characteristics may include: the type of plants, the color of the plant leaves, etc. It is possible to determine whether there is actually an abnormality in the first area based on the type of plants and the color of the plant leaves. After determining the type of plants, it is possible to determine whether there is a growth abnormality based on the color of the plant leaves in the normal state of this type of plant and the actual leaf color. When the color of the leaves in the normal state of this type of plant is different from the actual leaf color, it is possible to determine that this area is an area with abnormal plant growth.

[0025] S102: Monitor the area in the second area that meets the first condition based on the aerial drone monitoring system to obtain the first monitoring data; monitor the area in the second area that meets the second condition based on the mobile monitoring vehicle system to obtain the second monitoring data; monitor the area in the second area that meets the third condition based on the ground observation system to obtain the third monitoring data.

[0026] In this embodiment, the aerial drone monitoring system can be equipped with a variety of monitoring sensors. For example, soil moisture sensors, soil pH sensors, and soil fertility can be monitored simply by sampling the soil through the sampling device of the aerial drone monitoring system.

[0027] The mobile monitoring vehicle system is a vehicle equipped with various monitoring devices. It can travel on the ground and monitor the surrounding area during the driving process. The mobile monitoring vehicle system is equipped with soil moisture sensors, soil pH sensors, soil fertility monitors, crop physiological index detectors, etc., and can conduct on-site and relatively accurate monitoring of accessible areas such as near roads.

[0028] The ground observation system is an observation system set at a fixed position and can include a weather station, fixed soil observation points, long-term crop growth observation points, etc. It is used for long-term and stable monitoring of specific positions and can provide data in a certain time series, such as meteorological data at a fixed monitoring frequency, soil properties, and crop growth index data.

[0029] The first condition includes: the mobile monitoring vehicle system cannot reach and / or the ground observation system cannot monitor and / or the area is larger than the first area threshold; The second condition includes: the area is smaller than or the first area threshold and the mobile monitoring vehicle can reach; The third condition includes: being in the area monitored by the ground observation system.

[0030] Since whether the mobile monitoring vehicle can conduct effective data monitoring depends on whether it can reach the corresponding area and the area of the region, when, for example, the area to be monitored is a remote and rugged mountainous area or a large area without road coverage, the mobile monitoring vehicle cannot conduct effective and accurate data monitoring on this area. At this time, the area can be monitored through an aerial drone monitoring system.

[0031] The data monitored by the ground observation system is the most accurate, but its limitation is that it cannot monitor other areas and can only monitor the agricultural data of a fixed area through fixed monitoring frequencies and means. When the area to be monitored does not belong to the area that can be monitored by the ground observation system, the area can be monitored through an aerial drone monitoring system.

[0032] The first monitoring data is obtained through the aerial drone monitoring system, the second monitoring data is obtained through the mobile monitoring vehicle system, and the third monitoring data is obtained through the ground observation system. Among the first monitoring data, the second monitoring data, and the third monitoring data, the third monitoring data is the most accurate and has the richest amount of data.

[0033] S103: Obtain the agricultural monitoring result based on the first monitoring data, the second monitoring data, and the third monitoring data.

[0034] In this embodiment, obtaining the monitoring result based on the first monitoring data, the second monitoring data, and the third monitoring data includes: Obtain the first plant growth monitoring result of the area that meets the first condition based on the first monitoring data; Obtain the second plant growth monitoring result of the area that meets the second condition based on the second monitoring data; Obtain the third plant growth monitoring result of the area that meets the third condition based on the third monitoring data; Obtain the monitoring result based on the first plant growth monitoring result, the second plant growth monitoring result, and the third plant growth monitoring result.

[0035] The first monitoring data may include: plant characteristics and soil characteristics monitored by the aerial drone monitoring system. The second monitoring data may include: plant characteristics and soil characteristics monitored by the mobile monitoring vehicle system. The third monitoring data may include: external environmental characteristics, plant characteristics, and soil characteristics monitored by the ground observation system. Since both the aerial drone system and the mobile monitoring vehicle system are non-fixed monitoring methods, it is not very useful to obtain the external environmental characteristics at that time, and the results are also inaccurate. However, the ground observation system can monitor the plant growth in the area in a fixed monitoring form, so the external environmental characteristics it monitors are accurate.

[0036] The first plant growth monitoring result is obtained by monitoring with an aerial drone monitoring system, the second plant growth monitoring result is obtained by monitoring with a mobile monitoring vehicle system, and the third plant growth monitoring result is obtained based on a ground observation system.

[0037] The above three different sources of monitoring data covering different ranges form the monitoring data of the second region, and it is possible to judge whether there are abnormalities in the growth of plants based on each monitoring data.

[0038] It can be concluded from the above that the present disclosure realizes an all-round and multi-level coverage of the agricultural region through five different levels of monitoring means, namely, a space-based satellite system, an airborne remote sensing system, an aerial drone monitoring system, a mobile monitoring vehicle system, and a ground observation system, can obtain more comprehensive information on the agricultural region, and improve the comprehensiveness and accuracy of agricultural monitoring. After initially determining the agricultural region through the space-based and airborne systems, the present disclosure selects a suitable monitoring means for further monitoring according to different conditions, can more accurately locate the problem area, and improve the accuracy and reliability of agricultural monitoring.

[0039] In an embodiment of the present disclosure, the area in the second region that meets the first condition is monitored based on the aerial drone monitoring system to obtain first monitoring data, including: Obtaining the first plant characteristics and soil characteristics of the area that meets the first condition within the first time period based on the aerial drone monitoring system; Obtaining the external environment characteristics of the area that meets the first condition within the first time period based on the space-based satellite system; Determining the first monitoring data based on the first plant characteristics, soil characteristics, and external environment characteristics.

[0040] In this embodiment, the first time period is a preset time period, for example, it can be 7 days. The first plant characteristics can be the plant species, the plant growth stage, the height of the plant at the start of the first time period, and the height of the plant at the end of the first time period. The soil characteristics can be the soil fertility within the first time period, the soil acidity and alkalinity within the first time period, and the soil humidity within the first time period. The external environment characteristics can be the illumination time within the first time period, the illumination intensity within the first time period, and the environmental temperature within the first time period.

[0041] Since the monitoring frequency of the aerial drone monitoring system is limited within the first time period and the aerial drone system is limited by the battery life of its own, it is impossible to comprehensively monitor the factors affecting plant growth such as temperature, illumination time, and illumination intensity in the area. Therefore, the space-based satellite system can be used to supplement the temperature information, illumination intensity, illumination time, and other factors in this area to obtain accurate monitoring data.

[0042] The growth rate of the plant can be obtained based on the monitored first plant characteristics. The first plant characteristics, soil characteristics, and external environment characteristics can be compared with the standard growth rate within the first time period under the influence of the growth stage, soil characteristics, and external environment characteristics of the plant pre-stored. If the growth rate difference is large and exceeds the pre-set growth rate threshold, the plant growth state in this area is abnormal, that is, the first monitoring data is abnormal.

[0043] From the above, it can be concluded that the present disclosure obtains plant characteristics and soil characteristics through an aerial drone monitoring system, and at the same time combines the external environment characteristics provided by the space-based satellite system, which can form a comprehensive and accurate monitoring data set, making up for the deficiencies of the drone monitoring system in some monitoring parameters and improving the accuracy and reliability of agricultural monitoring. In this embodiment, by comparing the monitored plant characteristics, soil characteristics, and external environment characteristics with the standard growth rate, the growth state of the plant can be accurately evaluated, which helps to timely discover and handle abnormal plant growth conditions.

[0044] In an embodiment of the present disclosure, determining the first monitoring data based on the first plant characteristics, soil characteristics, and external environment characteristics includes: Determining a first growth characteristic based on the first plant characteristics; Determining a standard growth characteristic based on the first plant characteristics, soil characteristics, and external environment characteristics; In response to the first growth characteristic and the standard growth characteristic matching, the result of the first monitoring data is normal; In response to the first growth characteristic and the standard growth characteristic not matching, the result of the first monitoring data is abnormal.

[0045] In this embodiment, the first plant characteristics may include: plant species, plant growth stage, the height of the plant at the start of the first time period, and the height of the plant at the end of the first time period. The first growth characteristic may be the growth height of the plant within the first time period or some characteristics that should appear in this stage for certain plants. For example, for plant type A, characteristic C should appear before characteristic D in stage B, but during monitoring, it is found that for plant type A in stage B, characteristic D appears before characteristic C. At this time, the growth of the plant is abnormal.

[0046] The standard growth characteristics refer to the ideal growth characteristics of a plant under specific stages, specific soil characteristics, and specific external environmental characteristics. For example, for rice in the booting stage, when the soil fertility is at a medium level, the soil pH value is 6.5, the soil humidity is moderate, and at the same time, the light duration is 10 hours, the light intensity is medium, and the environmental temperature is 25°C. Through long-term statistics and research, it is known that its standard growth rate may be 1.5 cm / day. That is, when the soil fertility is at a medium level, the soil pH value is 6.5, the soil humidity is moderate, and at the same time, the light duration is 10 hours, the light intensity is medium, and the environmental temperature is 25°C, the standard growth characteristic of rice in the booting stage is 1.5 cm / day.

[0047] It is possible to determine whether the plant growth is abnormal by comparing the difference between the standard growth characteristics and the first growth characteristics with a preset growth rate threshold. For example: In response to the absolute value of the difference between the standard growth characteristics and the first growth characteristics being less than or equal to the growth rate threshold, the first growth characteristics and the standard growth characteristics match; In response to the absolute value of the difference between the standard growth characteristics and the first growth characteristics being greater than the growth rate threshold, the first growth characteristics and the standard growth characteristics match.

[0048] The growth rate threshold can be set according to the type of plant and experience. The growth rate of each plant is different at different growth stages. Similarly, different soil humidity, environmental temperature, soil pH, light duration, light intensity, and soil fertility will also affect the growth rate of plants.

[0049] From the above, it can be concluded that in this embodiment, by comparing the first growth characteristics with the standard growth characteristics, the growth state of the plant can be evaluated, which is more objective and accurate than traditional manual observation. It can timely detect and handle abnormal plant growth situations, and thus take corresponding measures to ensure the healthy growth of the plant. In this embodiment, the monitoring data results are determined by the method of multi-characteristic matching, which improves the efficiency and accuracy of agricultural monitoring. At the same time, in this embodiment, by setting the growth rate threshold, it is possible to more scientifically judge whether the plant growth is abnormal, avoiding misjudgment or missed judgment caused by subjective judgment, and improving the accuracy and reliability of agricultural monitoring.

[0050] In an embodiment of the present disclosure, determining the standard growth characteristics based on the first plant characteristics, soil characteristics, and external environmental characteristics includes: Determining a plurality of target standard growth characteristics from the standard database based on the first plant characteristics; Determining the standard growth characteristics from the plurality of target standard growth characteristics based on the soil characteristics and the external environmental characteristics.

[0051] In this embodiment, the first plant feature, including the type of the plant and the plant growth stage, can screen out multiple target standard growth features of the plant of this type from the standard feature library based on the type of the plant and the plant growth stage.

[0052] The standard database is a pre-established information library that stores a large amount of data on the ideal growth characteristics of different plants under various different conditions. It stores the standard growth characteristics of different plant species at different growth stages corresponding to different combinations of soil fertility, acidity, humidity, as well as different light time, intensity, environmental temperature and other conditions. For example, content such as standard growth rate and morphological characteristics that should appear provides a reference standard for judging whether the plant growth is normal in actual monitoring.

[0053] Based on the obtained first plant feature (mainly plant type and growth stage information), screening and matching are carried out in the standard database to find all possible relevant sets of standard growth characteristics that match the plant type and growth stage. Since in the standard database, for a certain growth stage of the same plant, there may be multiple corresponding standard growth performance descriptions due to various different combinations of other conditions such as soil and environment, so multiple target standard growth characteristics will be screened out. For example, for an apple tree in the flowering period, there may be multiple standard growth situations of the apple tree in the flowering period corresponding to different combinations of soil fertility, different light and temperature conditions in the standard database, and these are all screened out as multiple target standard growth characteristics.

[0054] The soil features can include: soil acidity, soil humidity and soil fertility. The external environment features can be temperature, light intensity and light time. When the first time period is 7 days, the temperature can be the average temperature in this area within 7 days, the light time can be the cumulative light time in this area within 7 days, and the light intensity can be the average value of the light intensity in this area within 7 days. And the acidity, humidity and fertility of the soil can be processed according to the length of the first time period. The first time period can be adjusted according to the plant type and the plant growth stage. For example: Determine the first initial time period; In response to the first plant feature satisfying the first growth condition, increase the first initial time period by the first growth step length to obtain the first time period; In response to the first plant feature not satisfying the first growth condition, use the first initial time period as the first time period; In response to the first time period being greater than the first time period threshold, calculate the average soil feature within the first time period as the soil feature; In response to the first time period being less than or equal to the first time period threshold, use the soil feature at the start of the first time period as the soil feature.

[0055] In this embodiment, it should be noted that the first time period is unknown before monitoring. After detecting the first plant growth characteristic, the first time period can be obtained based on the first plant growth characteristic. Similarly, before monitoring, it is not determined whether to monitor the soil characteristics within the first time period. The first growth condition can be that the plant species grows slowly at this stage, which can be obtained from the pre-stored information. When the monitored plant species grows relatively slowly at the current stage, if the first initial time period is still used for monitoring, the monitoring result may be inaccurate. Therefore, at this time, the first time period can be lengthened by the first growth step, and the first growth step can be determined through experiments.

[0056] In the first initial time period, for example, 3 days, the changes in soil pH, soil humidity, and soil fertility are relatively small. At this time, the soil pH, soil humidity, and soil fertility measured for the first time can be used as the data within these three days for matching. However, when the first time period is lengthened, the changes in soil pH, soil humidity, and soil fertility cannot be ignored. At this time, the soil characteristics can be monitored at a fixed monitoring frequency, and the average value can be calculated as the soil characteristics within the first time period for matching.

[0057] It can be concluded from the above that in this embodiment, by combining plant characteristics, soil characteristics, and external environment characteristics, the standard growth characteristics that best match the current monitoring conditions are selected from the standard database, realizing the accurate assessment of the plant growth state. In this embodiment, according to the characteristics of the plant species and growth stage, the monitoring time period is dynamically adjusted to ensure the effectiveness of the monitoring results. For plants with slow growth or specific growth stages, by extending the monitoring time period, the growth conditions of the plants can be more accurately reflected, avoiding misjudgment caused by too short a monitoring time period. Considering the plant species and growth stage, this embodiment also comprehensively considers various factors such as soil pH, humidity, fertility, and the temperature, light intensity, and light time of the external environment, realizing the comprehensive monitoring of the plant growth environment and improving the accuracy and reliability of agricultural monitoring.

[0058] In one embodiment of the present disclosure, determining the standard growth characteristics from multiple target standard growth characteristics based on soil characteristics and external environment characteristics includes: In response to the soil characteristics matching the standard soil characteristics and the external environment characteristics matching the standard external environment characteristics, determining the standard growth characteristics from multiple target standard growth characteristics based on the standard soil characteristics and the standard external environment characteristics; In response to the soil characteristics not matching the standard soil characteristics and / or the external environment characteristics not matching the standard external environment characteristics, determining the standard growth characteristic with the highest matching degree; Compensating the standard growth characteristic with the highest matching degree based on multiple standard soil characteristics and / or multiple standard environmental characteristics to obtain the standard growth characteristic.

[0059] In this embodiment, considering that the standard library may not contain all combinations of soil characteristics and external environment characteristics, the standard soil characteristics refer to the soil characteristics stored in the standard library, and the standard external environment characteristics refer to the external environment characteristics stored in the standard library. That is, when any one of the soil characteristics does not match the standard soil characteristics or the external environment characteristics does not match the standard external environment characteristics, a combination of soil characteristics and external environment characteristics with the highest matching degree can be determined, and the corresponding standard growth characteristics are used as the standard growth characteristics of the plant.

[0060] Or compensate the standard growth characteristics with the highest matching degree based on multiple standard soil characteristics and / or multiple standard external environment characteristics. For example: Obtain a first difference based on the soil characteristics and the first soil characteristics; Determine the first standard soil characteristics based on the first difference and the first soil characteristics; Determine a first difference value based on the standard growth characteristics corresponding to the first standard soil characteristics and the standard growth characteristics corresponding to the first soil characteristics; Compensate the standard growth characteristics corresponding to the first soil characteristics based on the first difference value to obtain the standard growth characteristics.

[0061] Obtain a second difference based on the external environment characteristics and the first external environment characteristics; Determine the first standard external environment characteristics based on the second difference and the first external environment characteristics; Determine a second difference value based on the standard growth characteristics corresponding to the first standard external environment characteristics and the standard growth characteristics corresponding to the first external environment characteristics; Compensate the standard growth characteristics corresponding to the first external environment characteristics based on the second difference value to obtain the standard growth characteristics.

[0062] In this embodiment, the first soil characteristics are the soil characteristics with the highest matching degree with the monitored soil characteristics, and the first standard soil characteristics are the soil characteristics whose difference from the first soil characteristics is also the first difference. The first external environment characteristics are the external environment characteristics with the highest matching degree with the monitored external environment characteristics, and the first standard external environment characteristics are the external environment characteristics whose difference from the first external environment characteristics is also the second difference.

[0063] For example, in standard growth characteristics, soil moisture affects aspects such as the growth rate of grape fruit diameter and fruit color. A decrease in soil moisture will slightly reduce the plant growth rate because the water supply is insufficient, affecting plant physiological activities such as photosynthesis and nutrient absorption. The monitored soil moisture is X, and all other condition standard libraries have it stored. However, when the soil moisture is X, there is no corresponding standard growth characteristic. The one most closely matching it is the standard growth characteristic when the soil moisture is X+Y, with the first difference being Y. At this time, the standard growth characteristic when the soil moisture is X+2Y can be found, and the difference in its standard growth characteristic is used as the first difference value, that is, the growth difference amount corresponding to a change of Y in soil moisture. The standard growth characteristic when the soil moisture is X+Y can be subtracted by the first difference value to obtain the standard growth characteristic when the soil moisture is X. The first difference value is the compensation value. In the present disclosure, the data processing methods of the mobile monitoring vehicle, the aerial drone, and the ground observation system are the same. The difference is that the ground observation system does not require the supplementation of external environmental characteristics of the space-based satellite system and can monitor accurate external environmental characteristics by itself.

[0064] It can be concluded from the above that the present disclosure determines the standard growth characteristic that best meets the current monitoring conditions from multiple target standard growth characteristics by comprehensively considering soil characteristics and external environmental characteristics. When the monitored soil or environmental characteristics do not exactly match those in the standard library, compensation adjustment can be performed based on the most highly matched characteristic, thereby obtaining a more accurate standard growth characteristic, enhancing the adaptability and flexibility of the method, and making the monitoring results more accurate and reliable. Through matching and compensation in this embodiment, resource waste caused by completely relying on standard library data is avoided. Even when the standard library data is incomplete or the monitoring conditions are significantly different from those in the standard library, a reasonable standard growth characteristic can still be obtained, reducing the monitoring cost caused by insufficient or mismatched data.

[0065] In an embodiment of the present disclosure, the agricultural monitoring method based on five-base collaborative integration further includes: Obtaining fourth monitoring data based on third monitoring data; Training a first neural network model based on the third monitoring data and the fourth monitoring data to obtain a second neural network model.

[0066] In an embodiment of the present disclosure, training a first neural network model based on the third monitoring data and the fourth monitoring data to obtain a second neural network model includes: Inputting the third monitoring data and the fourth monitoring data into the first neural network model, and training the first neural network model based on a first loss function to obtain a second neural network model; wherein, the weights of the loss function of the first neural network model are determined based on the third monitoring data and the fourth monitoring data.

[0067] In this embodiment, since the third monitoring data is obtained by the ground observation system and is the most accurate, the present disclosure trains the first neural network model with the third monitoring data. Since the third monitoring data is obtained by the ground observation system at a fixed monitoring frequency, the monitored data is discontinuous in the time line. At this time, the third monitoring data can be augmented to generate new data. For example, originally, the soil humidity in the third monitoring data was recorded every 6 hours. The data interpolation algorithm is used to supplement the missing time point data in the middle, so that the humidity data forms a continuously changing time series. The augmented data becomes part of the fourth monitoring data; or according to the historical data of crop pest and disease occurrence in the same period of this area, the missing relevant records in the current period are supplemented and also included in the fourth monitoring data.

[0068] Input the third monitoring data and the fourth monitoring data into the first neural network model for training. The first neural network model can be a convolutional neural network. The first loss function can be: , where is the weight parameter, which adjusts the importance of the original data and the augmented data in the loss function. The original data is the third monitoring data, and the augmented data is the fourth monitoring data. represents the output of the first neural network, is the value of the original data, is the value of the augmented data. represents the mean square error.

[0069] Since the fourth monitoring data is obtained by augmentation, during the training process, the weight of the third monitoring data should be higher, and the weight of the fourth monitoring data should be lower than the weight corresponding to the third monitoring data. That is, the real data should be considered more fully. The weight parameter can be calculated by the following formula : , where represents the number of the type of original data, represents the number of the type of augmented data, represents the total number of original data types, represents the total number of augmented data types.

[0070] For example, represents the number of original data of soil fertility. If it is monitored 3 times in 1 day, the number is 3. It represents the data volume of soil fertility that has been expanded 27 times within this day. So the quantity is 27. It should be noted that the ground observation system has different monitoring frequencies for different types of data. For example, the monitoring of temperature is more frequent, and in this case, the expanded data quantity may be less than the original data. When the function value output by the loss function tends to be stable, it represents the end of model training, and the second neural network model is obtained. The second neural network model can expand the data values when not monitored according to the actual monitoring values, and can be used not only for the ground observation system, but also for the aerial drone monitoring system and the mobile monitoring vehicle system.

[0071] As can be seen from the above, in this disclosure, by jointly using the third monitoring data provided by the ground observation system and the fourth monitoring data obtained through data expansion to train the first neural network model, and then obtaining the second neural network model, the accuracy of the model can be significantly improved. The accuracy of the original data provides a solid foundation for the model, while the expanded data helps the model learn more variation rules and features, enhancing the generalization ability of the model. During the training process, according to the characteristics of the third monitoring data and the fourth monitoring data, the weights of various types of data in the loss function are flexibly adjusted. Since the third monitoring data is real and accurate, a higher weight is given; while the fourth monitoring data is obtained through expansion, a lower weight is given, improving the accuracy and reliability of agricultural monitoring.

[0072] Corresponding to the agricultural monitoring method based on five-base collaborative integration in the above embodiment, Figure 2 The following is a structural block diagram of an agricultural monitoring system based on five-base collaborative integration provided by an embodiment of the present disclosure. For the sake of illustration, only the parts related to the embodiments of the present disclosure are shown. Refer to Figure 2 The agricultural monitoring system 20 based on five-base collaborative integration includes: a region determination module 21, a region monitoring module 22, and a result output module 23.

[0073] Among them, the region determination module 21 is used to monitor the target region based on the space-based satellite system to determine the first region, where the first region is a region within the target region; monitor the first region based on the air-based remote sensing system to determine the second region; the second region is a region within the first region, and the first region is an agricultural region; The region monitoring module 22 is used to monitor the regions in the second region that meet the first condition based on the aerial drone monitoring system to obtain the first monitoring data; monitor the regions in the second region that meet the second condition based on the mobile monitoring vehicle system to obtain the second monitoring data; monitor the regions in the second region that meet the third condition based on the ground observation system to obtain the third monitoring data; The result output module 23 is used to obtain the agricultural monitoring result based on the first monitoring data, the second monitoring data, and the third monitoring data.

[0074] In one embodiment of the present disclosure, the area monitoring module 22 is specifically configured to obtain the first plant characteristics and soil characteristics of the area satisfying the first condition within the first time period based on the aerial drone monitoring system; obtain the external environment characteristics of the area satisfying the first condition within the first time period based on the space-based satellite system; determine the first monitoring data based on the first plant characteristics, soil characteristics, and external environment characteristics.

[0075] In one embodiment of the present disclosure, the area monitoring module 22 is further specifically configured to determine the first growth characteristics based on the first plant characteristics; determine the standard growth characteristics based on the first plant characteristics, soil characteristics, and external environment characteristics; In response to the first growth characteristics matching the standard growth characteristics, the result of the first monitoring data is normal; In response to the first growth characteristics not matching the standard growth characteristics, the result of the first monitoring data is abnormal.

[0076] In one embodiment of the present disclosure, the area monitoring module 22 is further specifically configured to determine multiple target standard growth characteristics from the standard database based on the first plant characteristics; determine the standard growth characteristics from the multiple target standard growth characteristics based on the soil characteristics and external environment characteristics.

[0077] In one embodiment of the present disclosure, the area monitoring module 22 is further specifically configured to, in response to the soil characteristics matching the standard soil characteristics and the external environment characteristics matching the standard external environment characteristics, determine the standard growth characteristics from the multiple target standard growth characteristics based on the standard soil characteristics and standard external environment characteristics; in response to the soil characteristics not matching the standard soil characteristics and / or the external environment characteristics not matching the standard external environment characteristics, determine the standard growth characteristic with the highest matching degree; compensate the standard growth characteristic with the highest matching degree based on the multiple standard soil characteristics and / or multiple standard environmental characteristics to obtain the standard growth characteristic.

[0078] In one embodiment of the present disclosure, the agricultural monitoring system 20 based on the five-base collaborative integration further includes: a model training module; The model training module is configured to obtain the fourth monitoring data based on the third monitoring data; train the first neural network model based on the third monitoring data and the fourth monitoring data to obtain the second neural network model.

[0079] The model training module is specifically configured to input the third monitoring data and the fourth monitoring data into the first neural network model, and train the first neural network model based on the first loss function to obtain the second neural network model; wherein, the weights of the loss function of the first neural network model are determined based on the third monitoring data and the fourth monitoring data.

[0080] See Figure 3 , Figure 3 which is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. As Figure 3 shown, the electronic device 300 in this embodiment may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The above-mentioned processors 301, input devices 302, output devices 303, and memories 304 communicate with each other through the communication bus 305. The memory 304 is used to store computer programs, and the computer programs include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. Among them, the processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above system embodiments, such as Figure 2 the functions of the modules 21 to 23 shown.

[0081] It should be understood that in the embodiments of the present disclosure, the so-called processor 301 may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0082] The input device 302 may include a touchpad, a fingerprint acquisition sensor (for acquiring the fingerprint information and the direction information of the fingerprint of the user), a microphone, etc., and the output device 303 may include a display (such as an LCD), a speaker, etc.

[0083] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A part of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0084] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure may execute the implementation manners described in the first and second embodiments of the agricultural monitoring method based on five-base collaboration and integration provided by the embodiments of the present disclosure, and may also execute the implementation manner of the electronic device described in the embodiments of the present disclosure, which will not be elaborated herein.

[0085] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, all or part of the processes in the method of the above embodiments are implemented. It can also be completed by instructing relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0086] The computer-readable storage medium may be an internal storage unit of the electronic device in any of the foregoing embodiments, such as the hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the electronic device. Further, the computer-readable storage medium may also include both the internal storage unit and the external storage device of the electronic device. The computer-readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store the data that has been output or will be output.

[0087] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of the examples have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this disclosure.

[0088] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described electronic devices and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0089] In several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces or units, or can be electrical, mechanical, or other forms of connection.

[0090] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of this disclosure.

[0091] In addition, the functional units in various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0092] The above is only the specific implementation manner of this disclosure, but the protection scope of this disclosure is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope disclosed by this disclosure, and these modifications or substitutions should all be covered by the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be subject to the protection scope of the claims.

Claims

1. An agricultural monitoring method based on five-base coordinated integration, characterized in that: include: Monitoring the target area based on a space-based satellite system to determine a first area, where the first area is an area within the target area; Monitoring the first area based on an air-based remote sensing system to determine a second area; The second area is an area within the first area, and the first area is an agricultural area; Based on the aerial drone monitoring system, the area in the second area that meets the first condition is monitored to obtain first monitoring data; based on the mobile monitoring vehicle system, the area in the second area that meets the second condition is monitored to obtain second monitoring data; based on the ground observation system, the area in the second area that meets the third condition is monitored to obtain third monitoring data; Agricultural monitoring results are obtained based on the first monitoring data, the second monitoring data and the third monitoring data.

2. The agricultural monitoring method based on the coordinated integration of five bases as claimed in claim 1 is characterized in that: The aerial drone monitoring system monitors the area in the second area that meets the first condition to obtain first monitoring data, including: Acquire first plant characteristics and soil characteristics of an area meeting the first condition within a first period of time based on the aerial drone monitoring system; Acquire, based on the space-based satellite system, external environmental characteristics of an area that meets the first condition within the first time period; First monitoring data is determined based on the first plant characteristics, the soil characteristics, and the external environment characteristics.

3. The agricultural monitoring method based on the five-base coordinated integration as claimed in claim 2 is characterized in that: The determining of the first monitoring data based on the first plant characteristic, the soil characteristic and the external environment characteristic includes: determining a first growth characteristic based on the first plant characteristic; Determining a standard growth characteristic based on the first plant characteristic, the soil characteristic, and the external environment characteristic; In response to the first growth feature matching the standard growth feature, the first monitoring data result is normal; In response to the first growth feature not matching the standard growth feature, the first monitoring data result is abnormal.

4. The agricultural monitoring method based on the five-base coordinated integration as claimed in claim 3 is characterized in that: The determining of the standard growth feature based on the first plant feature, the soil feature and the external environment feature comprises: determining a plurality of target standard growth characteristics from a standard database based on the first plant characteristic; A standard growth feature is determined from the plurality of target standard growth features based on the soil feature and the external environment feature.

5. The agricultural monitoring method based on the five-base coordinated integration as claimed in claim 4 is characterized in that: The determining of the standard growth feature from the plurality of target standard growth features based on the soil feature and the external environment feature comprises: In response to the soil characteristic matching a standard soil characteristic and the external environment characteristic matching a standard external environment characteristic, determining a standard growth characteristic from among the plurality of target standard growth characteristics based on the standard soil characteristic and the standard external environment characteristic; In response to the soil characteristic not matching the standard soil characteristic and / or the external environment characteristic not matching the standard external environment characteristic, determining the standard growth characteristic with the highest matching degree; The standard growth feature with the highest matching degree is compensated based on a plurality of standard soil features and / or a plurality of standard environmental features to obtain a standard growth feature.

6. The agricultural monitoring method based on the coordinated integration of five bases as claimed in claim 1 is characterized in that: Also includes: Obtain fourth monitoring data based on the third monitoring data; The first neural network model is trained based on the third monitoring data and the fourth monitoring data to obtain a second neural network model.

7. The agricultural monitoring method based on the five-base coordinated integration as claimed in claim 6 is characterized in that: The training of the first neural network model based on the third monitoring data and the fourth monitoring data to obtain the second neural network model includes: The third monitoring data and the fourth monitoring data are input into a first neural network model, and the first neural network model is trained based on a first loss function to obtain a second neural network model; wherein the weight of the loss function of the first neural network model is determined based on the third monitoring data and the fourth monitoring data.

8. An agricultural monitoring system based on the coordinated integration of five bases, characterized in that: include: A region determination module, configured to monitor a target region based on a space-based satellite system and determine a first region, where the first region is a region within the target region; Monitoring the first area based on an air-based remote sensing system to determine a second area; the second area is an area within the first area, and the first area is an agricultural area; A regional monitoring module is used to monitor the area in the second area that meets the first condition based on the aerial drone monitoring system to obtain first monitoring data; monitor the area in the second area that meets the second condition based on the mobile monitoring vehicle system to obtain second monitoring data; and monitor the area in the second area that meets the third condition based on the ground observation system to obtain third monitoring data; A result output module is used to obtain agricultural monitoring results based on the first monitoring data, the second monitoring data and the third monitoring data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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