A high-yield and high-efficiency rapeseed cultivation integrated processing method and system

Abstract

The present invention discloses a high-yield and high-efficiency rapeseed cultivation integrated processing method and system, which belongs to the technical field of vegetable cultivation, and comprises the following steps: obtaining a surface abnormality threat index; obtaining a drone monitoring performance influencing factor and a drone adjustment parameter set, thereby performing drone adjustment, and obtaining aerial thermal imaging parameters of the rapeseed field, and analyzing to obtain a thermal imaging complexity index; obtaining a deviation difference, and comparing it with a preset difference interval in a database, to obtain a drone monitoring judgment result; if the drone monitoring judgment result is that the monitoring is qualified, the drone monitoring adjustment is not performed, and the rapeseed abnormality judgment result is output; if the drone monitoring judgment result is that the monitoring is unqualified, matching is performed based on the deviation difference to obtain a deviation adjustment parameter set, thereby obtaining a final corrected output result of the rapeseed, and displaying it, thereby solving the problem of rapeseed yield and rapeseed quality decline caused by untimely rapeseed supervision in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of vegetable cultivation, and in particular to a high-yield and high-efficiency rapeseed cultivation integrated processing method and system. Background Art

[0002] With the rapid development of agricultural modernization, rapeseed has been highly valued as an important economic crop. Since the growth of rapeseed is easily affected by many factors, the rapeseed cultivation process needs to be strictly controlled. The existing rapeseed cultivation system achieves rapeseed cultivation management through integrated water and fertilizer and temperature control or through shading, covering, seedling management, etc.

[0003] For example, the invention patent with publication number CN112005784B discloses a three-dimensional leafy vegetable cultivation system and its implementation method, which include: a three-dimensional cultivation tray, a cultivation shed, an integrated water and fertilizer system, a temperature system and an Internet of Things control system. The cultivation shed is installed on the upper end of the three-dimensional cultivation tray. The cultivation shed is an installation carrier for the integrated water and fertilizer system and the temperature system, and covers the top of the three-dimensional cultivation tray to form a closed cultivation space. The cultivation shed includes a shed body and a drive assembly. The shed body is installed on the drive assembly. The drive assembly, the integrated water and fertilizer system and the temperature system are all connected to the Internet of Things control system.

[0004] For example, the invention patent with publication number CN104855170B discloses a frame-based method for raising rapeseed blanket seedlings, including the following steps: preparing the seedling site; making the seedling frame; laying soil; sowing; watering; shading and covering; managing the seedlings; transplanting; and removing the frame. This method breaks away from the traditional approach of raising seedlings in seedling trays. By using low-cost, reusable materials to make the seedling frame, it addresses the high cost, low efficiency, and short seedling lengths of standard rice seedling trays used to raise rapeseed blanket seedlings. This reduces seedling costs and allows for the cultivation of rapeseed blanket seedlings with adjustable lengths.

[0005] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0006] In the existing technology, the cultivation and management of rapeseed usually requires manual on-site analysis and judgment. However, it is difficult to monitor a large area of rapeseed in a timely manner when manually cultivating and managing rapeseed, and it requires a lot of manpower. When rapeseed is infected with diseases and pests, the quality and yield of rapeseed will be affected due to untimely supervision of rapeseed. Therefore, there is a problem of reduced rapeseed yield and quality due to untimely supervision of rapeseed. Summary of the Invention

[0007] The embodiments of the present application provide a high-yield and efficient integrated processing method and system for rapeseed cultivation, thereby solving the problem of reduced rapeseed yield and quality due to untimely rapeseed supervision in the prior art, and achieving accurate and efficient monitoring of abnormal conditions in rapeseed fields.

[0008] The embodiment of the present application provides a high-yield and high-efficiency integrated processing method for rapeseed cultivation, comprising the following steps: using an intelligent plant protection robot to conduct patrol monitoring of rapeseed fields, obtaining surface abnormality parameters of the rapeseed fields, and analyzing them to obtain surface abnormality threat indicators; using drone technology to conduct aerial patrol monitoring of rapeseed fields, obtaining drone monitoring performance influencing parameters, analyzing to obtain drone monitoring performance influencing factors and drone adjustment parameter sets, thereby performing drone adjustments, and obtaining aerial thermal imaging parameters of the rapeseed fields, and analyzing to obtain thermal imaging complex indicators; analyzing the surface abnormality threat indicators and thermal imaging complex indicators to obtain deviation differences, and comparing them with difference intervals preset in a database to obtain drone monitoring judgment results; if the drone monitoring judgment result is that the monitoring is qualified, the drone monitoring adjustment is not performed, and the rapeseed abnormality judgment result is output; if the drone monitoring judgment result is that the monitoring is unqualified, matching is performed based on the deviation difference to obtain a deviation adjustment parameter set, thereby adjusting the drone equipment and conducting a second aerial patrol monitoring to obtain the final corrected output result of the rapeseed, which is displayed.

[0009] Furthermore, the surface anomaly parameters of the rapeseed field are obtained and analyzed to obtain the surface anomaly threat index. The specific steps include: sampling the rapeseed in the rapeseed field to obtain the surface anomaly parameters of the rapeseed field, the surface anomaly parameters of the rapeseed field including the number of leaf spots, the maximum diameter of the spots, the average leaf area and the plant height of each sampled rapeseed; obtaining the current planting cycle of the rapeseed, and obtaining the surface anomaly reference set corresponding to the rapeseed in each planting cycle interval preset in the database, and obtaining the surface anomaly reference set corresponding to the current planting cycle of the rapeseed by matching, recorded as the designated surface anomaly reference set, and analyzed with the surface anomaly parameters of the rapeseed field to obtain the surface anomaly threat index; the designated surface anomaly reference set includes a reference value for the number of leaf spots, a reference value for the spot diameter, a reference value for the leaf area and a reference value for the plant height; the surface anomaly threat index is used to characterize the degree of surface monitoring growth anomaly of the rapeseed.

[0010] Furthermore, the method of obtaining the parameters affecting the drone monitoring performance and analyzing them to obtain the drone monitoring performance influencing factors and the drone adjustment parameter set includes the following specific steps: obtaining the parameters affecting the drone monitoring performance, the drone monitoring performance influencing parameters including light intensity, wind speed, humidity, wireless signal strength, data transmission speed, spectrum utilization and electromagnetic interference intensity; obtaining the drone monitoring reference set preset in the database, and analyzing it with the drone monitoring performance influencing parameters to obtain the drone monitoring influencing index; obtaining the drone monitoring performance influencing factors and the drone adjustment parameter set based on the drone monitoring influencing index and matching it with the database; the drone monitoring reference set includes a light intensity reference value, a wind speed reference value, a humidity reference value, a wireless signal strength reference value, a data transmission speed reference value, a spectrum utilization reference value and an electromagnetic interference intensity reference value; the drone monitoring influencing index is used to characterize the degree to which the drone is affected during the rapeseed detection process.

[0011] Furthermore, the specific method for obtaining the UAV monitoring impact index is as follows:

[0012]

[0013] Wherein, WJ represents the UAV monitoring impact index, WQ represents light intensity, ΔWQ represents the light intensity reference value, WF represents wind speed, ΔWF represents wind speed reference value, WS represents humidity, ΔWS represents humidity reference value, WX represents wireless signal strength, ΔWX represents wireless signal strength reference value, WC represents data transmission speed, ΔWC represents data transmission speed reference value, WP represents spectrum utilization, ΔWP represents spectrum utilization reference value, WR represents electromagnetic interference intensity, ΔWR represents electromagnetic interference intensity reference value, and e represents a natural constant.

[0014] Furthermore, the UAV monitoring performance influencing factor and the UAV adjustment parameter set are obtained based on the UAV monitoring impact index and matched with the database. The specific steps include: obtaining each UAV monitoring impact index interval preset in the database and the UAV monitoring performance influencing reference factor and the UAV pre-adjustment parameter set corresponding to each UAV monitoring impact index interval, and matching them with the UAV monitoring impact index. If the UAV monitoring impact index is within a certain UAV monitoring impact index interval, the UAV monitoring performance influencing reference factor and the UAV pre-adjustment parameter set corresponding to the interval are obtained, and marked as the UAV monitoring performance influencing factor and the UAV adjustment parameter set; the UAV adjustment parameter set includes the camera resolution of the UAV and the network transmission signal strength.

[0015] Furthermore, the aerial thermal imaging parameters of the rapeseed field are obtained and analyzed to obtain the thermal imaging complexity index, and the specific steps include: obtaining the aerial thermal imaging parameters of the rapeseed field, the aerial thermal imaging parameters of the rapeseed field include the imaging temperature of each rapeseed sampling point, the area of the abnormal temperature region and the density of the abnormal temperature distribution block; obtaining the aerial thermal imaging reference set preset in the database, and analyzing it with the aerial thermal imaging parameters of the rapeseed field to obtain the thermal imaging complexity index; the aerial thermal imaging reference set includes the imaging temperature reference value, the abnormal temperature region area reference value and the abnormal temperature distribution block density reference value; the thermal imaging complexity index is used to characterize the degree of thermal imaging abnormality of the rapeseed field.

[0016] Furthermore, the specific steps of obtaining the drone monitoring judgment result include: obtaining each surface abnormal threat index interval preset in the database and the surface abnormal threat reference value corresponding to each surface abnormal threat index interval, and matching them with the surface abnormal threat index; if the surface abnormal threat index is within a certain surface abnormal threat index interval, then obtaining the surface abnormal threat reference value corresponding to the surface abnormal threat index interval as the surface abnormal threat value; obtaining each thermal imaging complexity index interval preset in the database and the thermal imaging complexity reference value corresponding to each thermal imaging complexity index interval, and matching them with the thermal imaging complexity index; if the thermal imaging complexity index is within a certain thermal imaging complexity index interval, then obtaining the thermal imaging complexity reference value corresponding to the thermal imaging complexity index interval as the thermal imaging complexity value; performing difference processing on the surface abnormal threat value and the thermal imaging complexity value. Processing, and mark the difference as a deviation difference; obtain the deviation difference interval preset in the database, and compare it with the deviation difference; if the deviation difference is within the deviation difference interval, the UAV monitoring judgment result is qualified, the UAV monitoring adjustment is not performed, and the rapeseed abnormality judgment result is output; if the deviation difference exceeds the deviation difference interval, the UAV monitoring judgment result is unqualified; obtain the surface anomaly threat threshold and thermal imaging complexity threshold preset in the database, and compare them with the surface anomaly threat index and thermal imaging complexity index respectively; if the surface anomaly threat index is less than the surface anomaly threat threshold and the thermal imaging complexity index is less than the thermal imaging complexity threshold, the rapeseed abnormality judgment result is normal rapeseed; if the surface anomaly threat index is above the surface anomaly threat threshold or the thermal imaging complexity index is above the thermal imaging complexity threshold, the rapeseed abnormality judgment result is rapeseed abnormal.

[0017] Furthermore, if the UAV monitoring judgment result is that the monitoring is unqualified, a deviation adjustment parameter set is obtained based on the deviation difference. The specific steps include: obtaining each deviation difference interval preset in the database and the deviation adjustment reference set corresponding to each deviation difference interval; if the UAV monitoring judgment result is that the monitoring is unqualified, the deviation difference is compared with each deviation difference interval. If the deviation difference is within a preset deviation difference interval, the deviation adjustment reference set corresponding to the interval is obtained as the deviation adjustment parameter set.

[0018] Furthermore, the drone equipment is adjusted and a secondary aerial patrol monitoring is performed to obtain the final corrected output result of the rapeseed, which is displayed. The specific steps include: performing a secondary adjustment on the drone equipment based on the deviation adjustment parameter set, performing a secondary aerial patrol monitoring through the drone equipment after the secondary adjustment, obtaining secondary thermal imaging parameters, and analyzing again based on the secondary thermal imaging parameters to obtain a thermal imaging complexity index, and recording it as a secondary execution thermal imaging complexity index; analyzing based on the surface anomaly threat index, the secondary execution thermal imaging complexity index, the surface anomaly threat threshold and the thermal imaging complexity threshold to obtain the final corrected output result, and displaying the final corrected output result; the secondary execution thermal imaging complexity index is used to characterize the degree of thermal imaging anomaly of the rapeseed detected after the secondary adjustment.

[0019] The embodiment of the present application provides a high-yield and high-efficiency rapeseed cultivation integrated processing system, comprising: a surface assessment module, a thermal imaging assessment module, a drone monitoring and judgment module, and a secondary adjustment module; wherein the surface assessment module is used to use an intelligent plant protection robot to conduct patrol monitoring of the rapeseed field, obtain surface abnormality parameters of the rapeseed field, and analyze to obtain surface abnormality threat indicators; the thermal imaging assessment module is used to use drone technology to conduct aerial patrol monitoring of the rapeseed field, obtain drone monitoring performance influencing parameters, analyze to obtain drone monitoring performance influencing factors and drone adjustment parameter sets, thereby performing drone adjustments, and obtaining aerial thermal parameters of the rapeseed field. Image parameters are analyzed to obtain thermal imaging complex indicators; the UAV monitoring and judgment module is used to analyze the surface abnormal threat indicators and thermal imaging complex indicators to obtain deviation differences, and compare them with the difference intervals preset in the database to obtain UAV monitoring judgment results; the secondary adjustment module is used to not perform UAV monitoring adjustments if the UAV monitoring judgment result is that the monitoring is qualified, and output the rapeseed abnormality judgment result; if the UAV monitoring judgment result is that the monitoring is unqualified, matching is performed based on the deviation difference to obtain a deviation adjustment parameter set, thereby adjusting the UAV equipment and conducting secondary aerial patrol monitoring to obtain the final corrected output result of the rapeseed and display it.

[0020] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0021] 1. The present invention provides a high-yield and high-efficiency integrated processing method for rapeseed cultivation. By performing difference processing on the surface abnormal threat value and the complex value of thermal imaging, and comparing them with the difference interval preset in the database, the drone monitoring judgment result is obtained, and the drone equipment is adjusted according to the monitoring judgment result and a secondary aerial patrol monitoring is carried out, thereby realizing accurate and efficient monitoring of abnormal conditions in rapeseed fields, and effectively solving the problem of reduced rapeseed yield and rapeseed quality caused by untimely rapeseed supervision in the existing technology.

[0022] 2. The present invention obtains the deviation difference intervals preset in the database and the deviation adjustment reference sets corresponding to the deviation difference intervals, and compares them with the deviation differences to obtain the deviation adjustment parameter set, and performs secondary adjustments on the UAV, and then performs secondary aerial patrol monitoring to obtain the final corrected output result, thereby avoiding the problem of inaccurate rapeseed status monitoring caused by unreasonable network or equipment configuration during the UAV monitoring process.

[0023] 3. By obtaining the parameters affecting drone monitoring performance and matching them with the preset drone monitoring reference set in the database, the drone monitoring performance influencing factors and drone adjustment parameter set are obtained, and the drone equipment is adjusted according to the drone adjustment parameter set, thereby ensuring the stability and adaptability of the drone equipment in different environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flow chart of a high-yield and high-efficiency rapeseed cultivation integrated processing method provided in an embodiment of the present application;

[0025] Figure 2 A schematic structural diagram of a high-yield and high-efficiency rapeseed cultivation integrated processing system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiments of the present application provide a high-yield and efficient integrated processing method and system for rapeseed cultivation, thereby solving the problem of reduced rapeseed yield and quality caused by untimely rapeseed supervision in the prior art, and achieving accurate and efficient monitoring of abnormal conditions in rapeseed fields.

[0027] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0028] like Figure 1As shown, it is a flow chart of a high-yield and high-efficiency rapeseed cultivation integrated processing method provided by an embodiment of the present application, the method comprising the following steps: using an intelligent plant protection robot to conduct patrol monitoring of the rapeseed field, obtaining surface abnormality parameters of the rapeseed field, and analyzing to obtain surface abnormality threat indicators; using drone technology to conduct aerial patrol monitoring of the rapeseed field, obtaining drone monitoring performance influencing parameters, analyzing to obtain drone monitoring performance influencing factors and drone adjustment parameter sets, thereby performing drone adjustments, and obtaining aerial thermal imaging parameters of the rapeseed field, and analyzing to obtain thermal imaging complex indicators; analyzing the surface abnormality threat indicators and thermal imaging complex indicators to obtain deviation differences, and comparing them with the difference intervals preset in the database to obtain drone monitoring judgment results; if the drone monitoring judgment result is that the monitoring is qualified, the drone monitoring adjustment is not performed, and the rapeseed abnormality judgment result is output; if the drone monitoring judgment result is that the monitoring is unqualified, matching is performed based on the deviation difference to obtain a deviation adjustment parameter set, thereby adjusting the drone equipment and conducting a second aerial patrol monitoring to obtain the final corrected output result of the rapeseed, which is displayed.

[0029] In this embodiment, by combining intelligent plant protection robots with drone technology, efficient patrol monitoring and data analysis of rapeseed fields are conducted, enabling real-time monitoring and precise assessment of rapeseed growth status. By using the intelligent plant protection robot to obtain abnormal surface parameters of the rapeseed field, and combining it with drone aerial thermal imaging technology to obtain aerial thermal imaging parameters of the rapeseed field, a comprehensive assessment of rapeseed growth conditions and potential risks can be made. Adjusting drone equipment based on parameters affecting drone monitoring performance ensures the accuracy and stability of monitoring results. This not only improves rapeseed supervision efficiency, reduces labor input, and mitigates the impact of pests and diseases on rapeseed yield and quality, but also enables intelligent management of the rapeseed cultivation process, significantly improving rapeseed production efficiency and quality.

[0030] By capturing abnormal surface parameters of rapeseed (such as the number of leaf spots, spot diameter, and percentage of leaf loss area), it can directly reflect the growth status of rapeseed plants and promptly detect anomalies caused by factors such as pests and diseases. This enables high-precision ground monitoring, helping farmers adjust their planting strategies in a timely manner. It more accurately displays the growth status of rapeseed, making it easier for agricultural managers to understand and operate. Thermal imaging technology, by capturing temperature changes in rapeseed leaves, can reveal information such as plant moisture status, photosynthetic efficiency, and vegetation cover. It is particularly advantageous for monitoring rapeseed in extreme weather conditions such as low temperatures and drought. Complex thermal imaging indicators can provide a comprehensive picture of heat distribution and temperature anomalies. Thermal imaging can quickly scan large areas of rapeseed fields without touching the plants, greatly improving monitoring efficiency. Compared to traditional manual inspections, thermal imaging not only covers a wider area but also enables monitoring in all weather conditions, providing round-the-clock data support.

[0031] Furthermore, surface anomaly parameters of the rapeseed field are obtained and analyzed to obtain surface anomaly threat indicators. The specific steps include: sampling the rapeseed in the rapeseed field to obtain surface anomaly parameters of the rapeseed field, which include the number of leaf spots, the maximum diameter of the spots, the average leaf area and the plant height of each sampled rapeseed; obtaining the current planting cycle of the rapeseed surface anomaly, and obtaining the constant reference set corresponding to each planting cycle interval of rapeseed preset in the database, and obtaining the surface anomaly reference set corresponding to the current planting cycle of rapeseed by matching, which is recorded as the designated surface anomaly reference set, and analyzed with the surface anomaly parameters of the rapeseed field to obtain the surface anomaly threat indicator; the designated surface anomaly reference set includes a reference value for the number of leaf spots, a reference value for the spot diameter, a reference value for the leaf area and a reference value for the plant height; the surface anomaly threat indicator is used to characterize the degree of surface monitoring growth anomaly of rapeseed.

[0032] In this embodiment, by sampling the rapeseed field and obtaining multiple surface abnormality parameters (such as the number of leaf spots, the maximum diameter of the spots, the average leaf area and the plant height, etc.), the growth status of the rapeseed can be evaluated in many aspects, which helps to identify problems in growth, such as pests and diseases, nutritional deficiencies and other problems.

[0033] By obtaining the current rapeseed crop cycle and matching it with a preset surface anomaly reference set in the database, the system can automatically determine the deviation of the current rapeseed growth status from historical data. This standardizes and quantifies the growth status of rapeseed fields, reduces the subjectivity and errors of manual assessments, and improves the efficiency and accuracy of monitoring. Through timely analysis of surface anomaly threat indicators, abnormalities in rapeseed growth can be detected early, especially in the early stages of rapeseed growth. For example, if abnormal leaf spots appear or plant height falls below the reference value, potential pests and diseases or growth factors can be quickly identified through data comparison and matching, thus providing early warning.

[0034] The number of leaf spots can be automatically identified by using image processing software (such as OpenCV) after the intelligent plant protection robot captures rapeseed images using a camera. The maximum spot diameter and average leaf area can also be automatically extracted by analyzing and extracting the images using image analysis software (such as Photoshop). Plant height can also be measured and statistically analyzed using radar waves emitted by the intelligent plant protection robot.

[0035] The surface anomaly threat index is obtained by analyzing the surface anomaly parameters of the rapeseed field, taking into account the mutual influence relationship between these parameters. For example, the greater the number of spots and the larger the maximum diameter of the spots, the more serious the rapeseed has suffered from pests and diseases or abnormal growth. The leaf area reflects the photosynthesis and overall growth of the rapeseed. Rapeseed is controlled at different growth stages of the plant. The height of the plant is closely related to its overall nutritional status and growth environment. If the leaf area is small, the number of spots is large, and the plant height is low, it means that the growth of the rapeseed has been affected by adverse factors (such as pests and diseases), resulting in abnormal growth. If the rapeseed plant is too tall, it will easily fall over.

[0036] Obtaining surface anomaly threat indicators, the specific methods include:

[0037]

[0038] Where DY represents the surface anomaly threat index, i represents the number of the sampled rapeseed, i = 1, 2, 3, ..., i max ,i max Indicates the total number of sampled rapeseed, YB i represents the number of leaf spots of the i-th sampled rapeseed, ΔYB represents the reference value of leaf spot number, YZ i represents the maximum diameter of the spot of the i-th sampled rapeseed, ΔYZ represents the reference value of the spot diameter, and YS i represents the average leaf area of the i-th sampled rapeseed, ΔYS represents the reference value of leaf area, and YH i represents the plant height of the i-th sampled rapeseed, ΔYH represents the reference value of plant height, and e represents the natural constant.

[0039] Furthermore, the parameters affecting the UAV monitoring performance are obtained, and the UAV monitoring performance influencing factors and the UAV adjustment parameter set are obtained through analysis. The specific steps include: obtaining the parameters affecting the UAV monitoring performance, which include light intensity, wind speed, humidity, wireless signal strength, data transmission speed, spectrum utilization and electromagnetic interference intensity; obtaining the preset UAV monitoring reference set in the database, and analyzing it with the UAV monitoring performance influencing parameters to obtain the UAV monitoring influencing index; based on the UAV monitoring influencing index and matching it with the database, obtaining the UAV monitoring performance influencing factors and the UAV adjustment parameter set; the UAV monitoring reference set includes light intensity reference value, wind speed reference value, humidity reference value, wireless signal strength reference value, data transmission speed reference value, spectrum utilization reference value and electromagnetic interference intensity reference value; the UAV monitoring influencing index is used to characterize the degree to which the UAV is affected during the rapeseed detection process.

[0040] In this embodiment, light intensity can be measured using a light sensor, wind speed can be measured using an anemometer, humidity can be measured using a humidity sensor, wireless signal strength and data transmission speed can be measured using a network speed meter (e.g., Fluke Networks OptiView XG Network Analysis Tablet, Fujitsu Network OptiView XG Network Analyzer), spectrum utilization can be analyzed using a spectrum analyzer (e.g., Keysight Technologies N9010A X-Series Signal Analyzers, Jabil N9010A Series Signal Analyzers), and electromagnetic interference intensity can be measured using an electromagnetic interference intensity meter. Spectrum utilization refers to the proportion of allocated frequency resources (e.g., radio spectrum) within a certain frequency band that is actually used to transmit information. It measures the efficiency of spectrum use, namely, the ratio of the amount of data actually transmitted to the total amount of available frequency within a certain frequency band.

[0041] Furthermore, the impact index of drone monitoring is obtained. The specific method is as follows:

[0042]

[0043] Wherein, WJ represents the UAV monitoring impact index, WQ represents light intensity, ΔWQ represents the light intensity reference value, WF represents wind speed, ΔWF represents wind speed reference value, WS represents humidity, ΔWS represents humidity reference value, WX represents wireless signal strength, ΔWX represents wireless signal strength reference value, WC represents data transmission speed, ΔWC represents data transmission speed reference value, WP represents spectrum utilization, ΔWP represents spectrum utilization reference value, WR represents electromagnetic interference intensity, ΔWR represents electromagnetic interference intensity reference value, and e represents a natural constant.

[0044] In this embodiment, the drone monitoring impact index is derived by analyzing parameters influencing drone monitoring performance (light intensity, wind speed, humidity, wireless signal strength, data transmission speed, spectrum utilization, and electromagnetic interference intensity). This takes into account the interplay between these parameters. For example, light intensity directly affects image acquisition quality. Excessive or insufficient light can reduce image quality, thereby affecting rapeseed detection accuracy. Wind speed affects drone stability. High wind speeds can lead to unstable flight, thus affecting the accuracy of monitoring results. High humidity increases flight control difficulty and affects camera clarity, leading to monitoring errors. Weak signal strength reduces data transmission speed, resulting in transmission delays or loss of real-time monitoring information, impacting monitoring efficiency. Excessive spectrum resources can cause signal interference, thereby affecting data transmission stability. Electromagnetic interference intensity affects the drone's wireless communications and sensor operation. Strong electromagnetic interference can affect drone control and data collection accuracy.

[0045] By acquiring and analyzing the parameters that affect UAV monitoring performance (including light intensity, wind speed, humidity, wireless signal strength, data transmission speed, spectrum utilization, and electromagnetic interference intensity), the potential impact of environmental factors on UAV monitoring performance is evaluated, which facilitates analysis of the extent to which UAVs are affected. When environmental factors change, UAV equipment or network equipment can be adjusted in a timely manner, enabling UAV equipment to monitor rapeseed status more efficiently and with higher quality.

[0046] Furthermore, based on the UAV monitoring impact index and matching with the database, the UAV monitoring performance impact factor and the UAV adjustment parameter set are obtained. The specific steps include: obtaining each UAV monitoring impact index interval preset in the database and the UAV monitoring performance impact reference factor and the UAV pre-adjustment parameter set corresponding to each UAV monitoring impact index interval, and matching them with the UAV monitoring impact index. If the UAV monitoring impact index is within a certain UAV monitoring impact index interval, the UAV monitoring performance impact reference factor and the UAV pre-adjustment parameter set corresponding to the interval are obtained, and marked as the UAV monitoring performance impact factor and the UAV adjustment parameter set; the UAV adjustment parameter set includes the UAV's camera resolution and the network transmission signal strength.

[0047] In this embodiment, by matching the drone's monitoring impact indicators with various pre-set monitoring impact indicator intervals in a database, the most appropriate drone monitoring performance impact factor and adjustment parameter set for the current environment can be automatically identified. This ensures that the drone always operates optimally under different environmental conditions, avoiding performance degradation caused by environmental changes or suboptimal monitoring conditions. Automatically matching the drone's monitoring performance impact factors and adjustment parameters based on changes in the monitoring impact indicators enables the drone to quickly adapt and adjust its operating strategy under varying climate, weather, and geographical conditions, ensuring the smooth execution of monitoring tasks, improving the accuracy of monitoring data collection and the stability of transmission, and reducing energy consumption and time waste during drone operation.

[0048] Furthermore, aerial thermal imaging parameters of the rapeseed field are obtained and analyzed to obtain thermal imaging complexity indicators. The specific steps include: obtaining aerial thermal imaging parameters of the rapeseed field, which include the imaging temperature of each rapeseed sampling point, the area of abnormal temperature region and the density of abnormal temperature distribution blocks; obtaining an aerial thermal imaging reference set preset in the database, and analyzing it with the aerial thermal imaging parameters of the rapeseed field to obtain thermal imaging complexity indicators; the aerial thermal imaging reference set includes imaging temperature reference values, abnormal temperature region area reference values and abnormal temperature distribution block density reference values; the thermal imaging complexity indicators are used to characterize the degree of thermal imaging abnormality in the rapeseed field.

[0049] In this embodiment, it should be noted that the imaging temperature at each rapeseed sampling point represents the temperature of the majority of the leaf surface, i.e., the temperature of the normal area of the leaf, which can be measured using an infrared thermal imager. The area of the abnormal temperature region can be obtained using image processing software (such as ThermoVision, whose Chinese name is "thermal imaging software"). The density of the abnormal temperature distribution block can be obtained by using images captured by a drone, identifying abnormal temperature areas using a thermal imager, and calculating the distribution of these areas, thereby obtaining the distribution density of the abnormal temperature blocks. The density of the abnormal temperature distribution block refers to the density of the distribution of the area of the abnormal temperature region.

[0050] Rapeseed leaf surfaces vary slightly from season to season, causing the imaging temperature at each rapeseed sampling point to change accordingly. Therefore, the imaging temperature reference value refers to the reference temperature corresponding to rapeseed during different growth cycles and seasons. For example, rapeseed leaf surface temperatures in summer are higher than those in winter. Therefore, the closer the rapeseed imaging temperature is to the temperature reference value, the better the rapeseed's condition. Abnormal temperature areas at rapeseed sampling points are caused by insects or inter-plant shading. Therefore, larger areas of abnormal temperature indicate more severe abnormalities. Greater density of abnormal temperature distribution blocks indicates a greater severity of pests and diseases in the rapeseed or more inappropriate rapeseed planting spacing.

[0051] The complex thermal imaging indicators obtained by analyzing the aerial thermal imaging parameters of rapeseed fields take into account the mutual influence relationship between these parameters. For example, changes in imaging temperature directly affect the identification of abnormal temperature areas. When rapeseed is infected with pests and diseases or there is occlusion between leaves, resulting in abnormal imaging temperature, an abnormal temperature area will be formed, which in turn affects the area of the abnormal temperature area. The larger the area of the abnormal temperature area, the higher the density of the abnormal temperature distribution blocks with a certain probability.

[0052] The specific method to obtain complex thermal imaging indicators is as follows:

[0053]

[0054] Where RF represents the thermal imaging complexity index, RW j represents the imaging temperature of the jth rapeseed sampling point, j represents the number of the rapeseed sampling point, j=1,2,3,...,j max ,j max represents the total number of rapeseed sampling points, ΔRW represents the imaging temperature reference value, RS j represents the area of abnormal temperature region at the jth rapeseed sampling point, ΔRS represents the reference value of abnormal temperature region area, ρ j represents the abnormal temperature distribution block density of the j-th rapeseed sampling point, Δρ represents the reference value of the abnormal temperature distribution block density, and e represents the natural constant.

[0055] By analyzing aerial thermal imaging parameters of rapeseed fields and deriving complex thermal imaging indicators, we can effectively characterize the degree of thermal anomalies in rapeseed fields, providing a scientific and accurate assessment of the plant's growth status and potential pests and diseases. Analyzing complex thermal imaging indicators in rapeseed fields allows for early detection of areas with abnormal leaf surface temperatures. For example, the presence of abnormal temperature areas may indicate that certain areas of the rapeseed are infested by pests or diseases or experiencing growth stunts. Early identification of these abnormal areas allows for appropriate control measures before the problem spreads, thus avoiding yield losses. Analysis of complex thermal imaging indicators enables a more scientific assessment of rapeseed's growth status and potential problems, enabling targeted measures to optimize management. Maintaining an optimal growing environment for rapeseed effectively promotes healthy growth and maximizes its yield and quality. Complex thermal imaging indicators enable more timely monitoring of farmland. With seasonal and climatic changes, as well as changes in rapeseed growth status, users can dynamically adjust management strategies based on real-time thermal imaging data.

[0056] Furthermore, the UAV monitoring judgment result is obtained, and the specific steps include: obtaining each surface abnormal threat index interval preset in the database and the surface abnormal threat reference value corresponding to each surface abnormal threat index interval, and matching them with the surface abnormal threat index. If the surface abnormal threat index is within a certain surface abnormal threat index interval, then the surface abnormal threat reference value corresponding to the surface abnormal threat index interval is obtained as the surface abnormal threat value; obtaining each thermal imaging complex index interval preset in the database and the thermal imaging complex reference value corresponding to each thermal imaging complex index interval, and matching them with the thermal imaging complex index. If the thermal imaging complex index is within a certain thermal imaging complex index interval, then the thermal imaging complex reference value corresponding to the thermal imaging complex index interval is obtained as the thermal imaging complex value; performing difference processing on the surface abnormal threat value and the thermal imaging complex value. , and mark the difference as a deviation difference; obtain the deviation difference interval preset in the database, and compare it with the deviation difference; if the deviation difference is within the deviation difference interval, the UAV monitoring judgment result is qualified, the UAV monitoring adjustment is not performed, and the rapeseed abnormality judgment result is output; if the deviation difference exceeds the deviation difference interval, the UAV monitoring judgment result is unqualified; obtain the surface anomaly threat threshold and thermal imaging complexity threshold preset in the database, and compare them with the surface anomaly threat index and thermal imaging complexity index respectively; if the surface anomaly threat index is less than the surface anomaly threat threshold and the thermal imaging complexity index is less than the thermal imaging complexity threshold, the rapeseed abnormality judgment result is normal rapeseed; if the surface anomaly threat index is above the surface anomaly threat threshold or the thermal imaging complexity index is above the thermal imaging complexity threshold, the rapeseed abnormality judgment result is rapeseed abnormal.

[0057] In this embodiment, it should be noted that by matching the surface anomaly threat index and thermal imaging complexity index with preset reference values in the database to obtain surface anomaly threat values and thermal imaging complexity values, and performing difference processing to obtain deviation differences, the drone monitoring judgment results obtained from this analysis can more accurately judge the monitoring results of rapeseed fields, avoiding the subjectivity and inconsistency of manual judgment, improving the accuracy and reliability of drone monitoring results, and providing a scientific basis for the timely detection of rapeseed anomalies. Automatically determining the health status of rapeseed based on monitoring data reduces the need for manual inspections and frequent interventions, thereby avoiding unnecessary waste and excessive management, and achieving the effect of saving costs and improving resource utilization efficiency.

[0058] It should be noted that by matching the surface anomaly threat index and the thermal imaging complexity index with the surface anomaly threat index intervals and the thermal imaging complexity index intervals preset in the database, the corresponding surface anomaly threat reference values and thermal imaging complexity reference values are obtained, thereby converting the surface anomaly threat index and the thermal imaging complexity index into indicators of the same dimension.

[0059] The surface abnormality threat index and thermal imaging complexity index represent different characteristics of rapeseed fields (such as surface growth abnormalities and thermal imaging temperature abnormalities). By converting the surface abnormality threat index and thermal imaging complexity index into evaluation results of the same dimension, the growth status of rapeseed can be judged more clearly, which helps managers make quick and accurate decisions, thereby improving the production efficiency and health management level of rapeseed.

[0060] Furthermore, if the UAV monitoring judgment result is that the monitoring is unqualified, the deviation adjustment parameter set is obtained based on the deviation difference. The specific steps include: obtaining each deviation difference interval preset in the database and the deviation adjustment reference set corresponding to each deviation difference interval; if the UAV monitoring judgment result is that the monitoring is unqualified, the deviation difference is compared with each deviation difference interval. If the deviation difference is within a preset deviation difference interval, the deviation adjustment reference set corresponding to the interval is obtained as the deviation adjustment parameter set.

[0061] In this embodiment, by obtaining a deviation adjustment parameter set based on deviation difference matching, the relevant parameters of the drone monitoring can be accurately adjusted in response to unqualified monitoring results, ensuring that the drone meets the preset standards more closely during the monitoring process, thereby improving the monitoring pass rate and data accuracy, and providing higher flexibility and adaptability for monitoring tasks under changing environments and conditions.

[0062] Furthermore, the UAV equipment is adjusted and a secondary aerial patrol monitoring is carried out to obtain the final corrected output result of the rapeseed, which is displayed. The specific steps include: making a secondary adjustment to the UAV equipment based on the deviation adjustment parameter set, conducting a secondary aerial patrol monitoring through the UAV equipment after the secondary adjustment, obtaining secondary thermal imaging parameters, and analyzing again based on the secondary thermal imaging parameters to obtain a thermal imaging complexity index, which is recorded as a secondary execution thermal imaging complexity index; analyzing based on the surface anomaly threat index, the secondary execution thermal imaging complexity index, the surface anomaly threat threshold and the thermal imaging complexity threshold to obtain the final corrected output result, and displaying the final corrected output result; the secondary execution thermal imaging complexity index is used to characterize the degree of thermal imaging anomaly of the rapeseed detected after the secondary adjustment.

[0063] In this embodiment, it should be noted that by performing a secondary adjustment on the drone equipment based on the deviation adjustment parameter set (image resolution, imaging angle of view, and flight altitude), the drone's performance can be further optimized when the initial monitoring results are unsatisfactory, eliminating errors in the equipment and parameter settings, thereby ensuring the drone's accuracy in subsequent monitoring, improving the reliability of the monitoring results, and ensuring the accuracy and consistency of the rapeseed field monitoring results. After the secondary adjustment, the drone equipment can conduct aerial patrol monitoring again and collect new thermal imaging parameters, effectively reducing the potential misjudgment problems in the initial monitoring. Especially when the rapeseed field faces complex environmental changes, the secondary monitoring helps avoid erroneous thermal imaging complex indicators, making the final corrected output results more accurate and reducing potential risks in the decision-making process.

[0064] The display of the final corrected output not only provides more accurate monitoring results but also enhances data visualization. By displaying the corrected results in real time, users can more intuitively understand the monitoring status of the rapeseed fields, enabling them to take more effective management measures based on the actual monitoring results, thus helping them better understand the monitoring data.

[0065] Based on the surface anomaly threat index, the secondary execution thermal imaging complexity index, the surface anomaly threat threshold and the thermal imaging complexity threshold, analysis is performed to obtain the final corrected output result. The specific steps include: obtaining the surface anomaly threat threshold and the thermal imaging complexity threshold preset in the database, and comparing them with the surface anomaly threat index and the secondary execution thermal imaging complexity index respectively; if the surface anomaly threat index is less than the surface anomaly threat threshold and the secondary execution thermal imaging complexity index is less than the thermal imaging complexity threshold, the rapeseed anomaly judgment result is that the rapeseed is normal; if the surface anomaly threat index is above the surface anomaly threat threshold or the secondary execution thermal imaging complexity index is above the thermal imaging complexity threshold, the rapeseed anomaly judgment result is that the rapeseed is abnormal.

[0066] It should be noted that the secondary thermal imaging parameters include: the imaging temperature review value of each rapeseed sampling point, the abnormal temperature area review value and the abnormal temperature distribution block density review value.

[0067] The complex index of secondary thermal imaging is obtained by:

[0068]

[0069] Where XF represents the complex index of secondary thermal imaging, Y represents the influencing factor of UAV monitoring performance, and XW j represents the imaging temperature review value of the j-th rapeseed sampling point, j represents the number of the rapeseed sampling point, j = 1, 2, 3, ..., j max ,j maxrepresents the total number of rapeseed sampling points, ΔXW represents the imaging temperature reference value, XS represents the abnormal temperature area review value of the j-th rapeseed sampling point, ΔXS represents the abnormal temperature area reference value, Xρ j represents the review value of the abnormal temperature distribution block density of the j-th rapeseed sampling point, ΔXρ represents the reference value of the abnormal temperature distribution block density, and e represents a natural constant.

[0070] It should be noted that the secondary thermal imaging parameters are the data obtained when the UAV equipment conducts secondary aerial patrol monitoring. For example: the imaging temperature review value refers to the temperature detected when the UAV equipment conducts secondary aerial patrol monitoring, the abnormal temperature area review value refers to the abnormal temperature area detected when the UAV equipment conducts secondary aerial patrol monitoring, and the abnormal temperature distribution block density review value refers to the abnormal temperature distribution block density detected when the UAV equipment conducts secondary aerial patrol monitoring.

[0071] like Figure 2 As shown, it is a structural diagram of a high-yield and high-efficiency rapeseed cultivation integrated processing system provided by an embodiment of the present application. The high-yield and high-efficiency rapeseed cultivation integrated processing system provided by an embodiment of the present application includes: a surface evaluation module, a thermal imaging evaluation module, a UAV monitoring and judgment module and a secondary adjustment module; wherein, the surface evaluation module is used to use an intelligent plant protection robot to conduct patrol monitoring of the rapeseed field, obtain surface abnormality parameters of the rapeseed field, and analyze to obtain surface abnormality threat indicators; the thermal imaging evaluation module is used to use UAV technology to conduct aerial patrol monitoring of the rapeseed field, obtain UAV monitoring performance influencing parameters, analyze to obtain UAV monitoring performance influencing factors and UAV adjustment parameter sets, thereby The drone is adjusted and the aerial thermal imaging parameters of the rapeseed field are obtained, and the thermal imaging complex indicators are analyzed; the drone monitoring and judgment module is used to analyze the surface abnormal threat indicators and the thermal imaging complex indicators to obtain the deviation difference, and compare it with the preset difference interval in the database to obtain the drone monitoring judgment result; the secondary adjustment module is used to not perform the drone monitoring adjustment if the drone monitoring judgment result is that the monitoring is qualified, and output the rapeseed abnormality judgment result; if the drone monitoring judgment result is that the monitoring is unqualified, the deviation adjustment parameter set is obtained based on the deviation difference, thereby adjusting the drone equipment and conducting secondary aerial patrol monitoring to obtain the final corrected output result of the rapeseed and display it.

[0072] To sum up, this embodiment obtains the UAV monitoring and judgment results by performing difference processing on the surface abnormal threat indicators and the thermal imaging complexity indicators, and compares them with the preset difference intervals in the database, so as to adjust the UAV equipment according to the monitoring and judgment results and conduct secondary aerial patrol monitoring, thereby realizing accurate and efficient monitoring of abnormal conditions in rapeseed fields, and effectively solving the problem of reduced rapeseed yield and quality caused by untimely rapeseed supervision in the existing technology.

[0073] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0077] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0078] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A high-yield and high-efficiency integrated processing method for rapeseed cultivation, characterized in that: The following steps are involved: Use intelligent plant protection robots to conduct patrol monitoring of rapeseed fields, obtain abnormal surface parameters of rapeseed fields, and analyze them to obtain surface abnormality threat indicators; Using drone technology to conduct aerial patrol monitoring of rapeseed fields, we obtained parameters influencing drone monitoring performance, analyzed the factors influencing drone monitoring performance and the drone adjustment parameter set, and used these to adjust the drones. We also obtained aerial thermal imaging parameters of the rapeseed fields and analyzed them to obtain complex thermal imaging indicators. Analyze the surface anomaly threat indicators and thermal imaging complexity indicators to obtain the deviation difference, and compare it with the preset difference range in the database to obtain the UAV monitoring judgment result; If the drone monitoring result is qualified, the drone monitoring adjustment will not be performed, and the rapeseed abnormality determination result will be output. If the drone monitoring result is unqualified, the deviation adjustment parameter set is obtained based on the deviation difference, and the drone equipment is adjusted accordingly and a second aerial patrol monitoring is carried out to obtain the final corrected output result of the rapeseed and display it; The steps of obtaining the parameters influencing the UAV monitoring performance and analyzing the UAV monitoring performance influencing factors and the UAV adjustment parameter set include: Obtaining parameters affecting drone monitoring performance, wherein the parameters affecting drone monitoring performance include light intensity, wind speed, humidity, wireless signal strength, data transmission speed, spectrum utilization, and electromagnetic interference intensity; Obtain the preset UAV monitoring reference set in the database and analyze it with the UAV monitoring performance influencing parameters to obtain the UAV monitoring impact index; Based on the UAV monitoring impact indicators and matching with the database, the UAV monitoring performance impact factors and the UAV adjustment parameter set are obtained; The drone monitoring reference set includes a light intensity reference value, a wind speed reference value, a humidity reference value, a wireless signal strength reference value, a data transmission speed reference value, a spectrum utilization reference value, and an electromagnetic interference intensity reference value; The UAV monitoring impact index is used to characterize the degree to which the UAV is affected during the process of UAV monitoring of rapeseed; The specific method for obtaining the UAV monitoring impact index is as follows: Wherein, WJ represents the UAV monitoring impact index, WQ represents light intensity, ΔWQ represents the light intensity reference value, WF represents wind speed, ΔWF represents wind speed reference value, WS represents humidity, ΔWS represents humidity reference value, WX represents wireless signal strength, ΔWX represents wireless signal strength reference value, WC represents data transmission speed, ΔWC represents data transmission speed reference value, WP represents spectrum utilization, ΔWP represents spectrum utilization reference value, WR represents electromagnetic interference intensity, ΔWR represents electromagnetic interference intensity reference value, and e represents a natural constant. The specific calculation method of surface anomaly threat index includes: ； Where DY represents the surface anomaly threat index, i represents the number of the sampled rapeseed, i = 1, 2, 3, ..., i max ,i max Indicates the total number of sampled rapeseed, YB i represents the number of leaf spots of the i-th sampled rapeseed, ΔYB represents the reference value of leaf spot number, YZ i represents the maximum diameter of the spot of the i-th sampled rapeseed, ΔYZ represents the reference value of the spot diameter, and YS i represents the average leaf area of the i-th sampled rapeseed, ΔYS represents the reference value of leaf area, and YH i represents the plant height of the i-th sampled rapeseed, ΔYH represents the reference value of plant height, and e represents the natural constant.

2. The high-yield and high-efficiency integrated rapeseed cultivation method according to claim 1, characterized in that: The steps of obtaining the surface anomaly parameters of the rapeseed field and analyzing them to obtain the surface anomaly threat index include: Sampling rapeseed in a rapeseed field to obtain surface abnormality parameters of the rapeseed field, wherein the surface abnormality parameters of the rapeseed field include the number of leaf spots, the maximum diameter of the spots, the average leaf area, and the plant height of each sampled rapeseed; Obtain the current planting cycle of rapeseed and obtain the surface anomaly reference set corresponding to each planting cycle interval of rapeseed preset in the database. By matching, obtain the surface anomaly reference set corresponding to the current planting cycle of rapeseed, record it as the designated surface anomaly reference set, and analyze it with the surface anomaly parameters of the rapeseed field to obtain the surface anomaly threat index; The designated surface anomaly reference set includes a reference value for the number of leaf spots, a reference value for the diameter of the spots, a reference value for the leaf area, and a reference value for the plant height; The surface abnormality threat index is used to characterize the abnormality degree of surface monitoring growth of rapeseed.

3. The high-yield and high-efficiency integrated rapeseed cultivation method according to claim 1, characterized in that: The method of obtaining the UAV monitoring performance influencing factors and the UAV adjustment parameter set based on the UAV monitoring impact indicators and matching with the database includes the following specific steps: Obtain each UAV monitoring impact index interval preset in the database and the UAV monitoring performance impact reference factor and UAV pre-adjustment parameter set corresponding to each UAV monitoring impact index interval, and match them with the UAV monitoring impact index. If the UAV monitoring impact index is within a certain UAV monitoring impact index interval, obtain the UAV monitoring performance impact reference factor and UAV pre-adjustment parameter set corresponding to the interval, and mark them as UAV monitoring performance impact factor and UAV adjustment parameter set; The drone adjustment parameter set includes the drone's camera resolution and network transmission signal strength.

4. The high-yield and high-efficiency integrated rapeseed cultivation method according to claim 1, characterized in that: The steps of obtaining aerial thermal imaging parameters of the rapeseed field and analyzing the complex thermal imaging indicators include: Acquiring aerial thermal imaging parameters of the rapeseed field, wherein the aerial thermal imaging parameters of the rapeseed field include imaging temperature of each rapeseed sampling point, area of abnormal temperature region, and density of abnormal temperature distribution blocks; Obtain the preset aerial thermal imaging reference set in the database and analyze it with the aerial thermal imaging parameters of the rapeseed field to obtain the thermal imaging complex index; The aerial thermal imaging reference set includes an imaging temperature reference value, an abnormal temperature region area reference value, and an abnormal temperature distribution block density reference value; The thermal imaging complexity index is used to characterize the degree of thermal imaging abnormality in the rapeseed field.

5. The high-yield and high-efficiency integrated rapeseed cultivation method according to claim 1, characterized in that: The specific steps of obtaining the drone monitoring and judgment result include: Obtain each surface anomaly threat index interval preset in the database and the surface anomaly threat reference value corresponding to each surface anomaly threat index interval, and match them with the surface anomaly threat index. If the surface anomaly threat index is within a certain surface anomaly threat index interval, obtain the surface anomaly threat reference value corresponding to the surface anomaly threat index interval as the surface anomaly threat value; Obtaining each thermal imaging complexity index interval preset in the database and the corresponding thermal imaging complexity reference value, and matching them with the thermal imaging complexity index. If the thermal imaging complexity index is within a certain thermal imaging complexity index interval, obtaining the thermal imaging complexity reference value corresponding to the thermal imaging complexity index interval as the thermal imaging complexity value; Perform difference processing on the surface abnormal threat value and the thermal imaging complex value, and mark the difference as a deviation difference; Obtain the preset deviation difference interval in the database and compare it with the deviation difference. If the deviation difference is within the deviation difference interval, the drone monitoring result is judged as qualified, and the drone monitoring adjustment is not performed, and the rapeseed abnormality judgment result is output. If the deviation difference exceeds the deviation difference interval, the drone monitoring result is judged as unqualified. The surface anomaly threat threshold and thermal imaging complexity threshold preset in the database are obtained and compared with the surface anomaly threat index and thermal imaging complexity index respectively. If the surface anomaly threat index is less than the surface anomaly threat threshold and the thermal imaging complexity index is less than the thermal imaging complexity threshold, the rapeseed anomaly judgment result is that the rapeseed is normal. If the surface anomaly threat index is above the surface anomaly threat threshold or the thermal imaging complexity index is above the thermal imaging complexity threshold, the rapeseed anomaly judgment result is that the rapeseed is abnormal.

6. The high-yield and high-efficiency integrated rapeseed cultivation method according to claim 1, characterized in that: If the UAV monitoring result is unqualified, a deviation adjustment parameter set is obtained by matching based on the deviation difference. The specific steps include: Obtaining each deviation difference interval preset in the database and a deviation adjustment reference set corresponding to each deviation difference interval; If the UAV monitoring result is unqualified, the deviation difference is compared with each deviation difference interval. If the deviation difference is within a preset deviation difference interval, the deviation adjustment reference set corresponding to the interval is obtained as the deviation adjustment parameter set.

7. The high-yield and high-efficiency integrated rapeseed cultivation method according to claim 1, characterized in that: The above steps include adjusting the UAV equipment and performing a second round of aerial monitoring to obtain the final corrected output result of rapeseed and displaying it. A secondary adjustment is performed on the UAV equipment based on the deviation adjustment parameter set, and a secondary aerial patrol monitoring is performed using the UAV equipment after the secondary adjustment to obtain secondary thermal imaging parameters. The secondary thermal imaging parameters are then analyzed again to obtain a thermal imaging complex index, which is recorded as a secondary execution thermal imaging complex index. Based on the surface anomaly threat index, the secondary execution thermal imaging complexity index, the surface anomaly threat threshold and the thermal imaging complexity threshold, the final correction output result is obtained and displayed; The secondary execution thermal imaging complexity index is used to characterize the abnormality degree of the rapeseed field thermal imaging detected after the secondary adjustment.

8. A system using a high-yield and high-efficiency rapeseed cultivation integrated processing method according to any one of claims 1 to 7, characterized in that: include: Surface assessment module, thermal imaging assessment module, drone monitoring and judgment module, and secondary adjustment module; The surface assessment module is used to use the intelligent plant protection robot to conduct patrol monitoring of the rapeseed field, obtain surface abnormality parameters of the rapeseed field, and analyze them to obtain surface abnormality threat indicators; The thermal imaging assessment module is used to conduct aerial patrol monitoring of rapeseed fields using drone technology, obtain parameters affecting drone monitoring performance, analyze to obtain drone monitoring performance influencing factors and drone adjustment parameter sets, and adjust the drone accordingly. It also obtains aerial thermal imaging parameters of the rapeseed fields and analyzes to obtain thermal imaging complex indicators. The UAV monitoring and judgment module is used to analyze the surface abnormal threat index and the thermal imaging complexity index to obtain the deviation difference, and compare it with the difference interval preset in the database to obtain the UAV monitoring judgment result; The secondary adjustment module is used to not perform drone monitoring adjustment if the drone monitoring judgment result is qualified, and output the rapeseed abnormality judgment result; if the drone monitoring judgment result is unqualified, match the deviation difference to obtain the deviation adjustment parameter set, thereby adjusting the drone equipment and conducting secondary aerial patrol monitoring to obtain the final corrected output result of the rapeseed and display it.

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