Unmanned aerial vehicle-based rice planting area monitoring management system and method

Abstract

The invention relates to the technical field of rice monitoring, and particularly discloses a rice planting area monitoring management system and method based on an unmanned aerial vehicle, and the system comprises an area division module which is used for dividing a rice planting area into a plurality of monitoring areas with the same size; the unmanned aerial vehicle shooting module comprises a shooting unit and an identification unit, and the shooting unit is used for shooting rice image information in different areas; the rice pest influence coefficients in different monitoring areas are calculated in combination with diversified data, and are compared with a preset pest influence coefficient threshold interval, so that pest risks in different monitoring areas can be accurately analyzed; and according to the grade of the insect pest risk, whether the insect pests need to be removed by using pesticides is judged, so that the situation that crops, soil, water sources and the like are polluted due to the fact that the insect pests are removed by blindly using the pesticides when the insect pests are in the initial stage is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of rice monitoring, and in particular to a rice planting area monitoring management system and method based on unmanned aerial vehicle. Background Art

[0002] Rice is one of the important food crops for mankind. It has a long history of cultivation and consumption. Half of the world's population eats rice. In order to ensure rice production, rice planting areas are usually monitored and managed in real time through rice planting area monitoring and management systems, aiming to improve the efficiency, yield and quality of rice planting.

[0003] During the rice planting process, insect pests have a considerable impact on the survival and yield of rice. The rice planting area monitoring and management system can capture the images and dynamic information of pests by integrating advanced technologies such as sensor technology, image processing technology and artificial intelligence technology. The data processing unit then processes and analyzes the collected data, and decides whether to carry out pest control operations based on the results, thereby realizing intelligent monitoring and management of rice pests.

[0004] Although the common rice-growing area monitoring and management systems in the prior art can detect insect pests in rice in a timely manner, they are unable to analyze and evaluate the risks of insect pests. If the insect pests are in the early stages, the number is small and the impact on crops is not great, blindly using pesticides to eliminate the pests may cause pollution to crops, soil, water sources, etc. Summary of the invention

[0005] The purpose of the present invention is to provide a rice planting area monitoring management system and method based on drones to solve the following technical problems:

[0006] How to analyze the risk of pests and choose the appropriate pest control method.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A rice planting area monitoring and management system and method based on unmanned aerial vehicle, the system comprising:

[0009] A region division module is used to divide the rice planting area into multiple monitoring areas of the same size;

[0010] The drone shooting module includes a camera unit and a recognition unit, wherein the camera unit is used to shoot rice image information in different areas;

[0011] The recognition unit is used to recognize the rice graphic information photographed by the camera unit and save the required data;

[0012] Environmental information collection module, used to collect environmental data of rice-growing areas at different time points;

[0013] A data processing module is used to calculate the impact coefficient of rice pests in different monitoring areas by combining the data collected by the drone shooting module with the data collected by the environment collection module;

[0014] The data analysis module is used to compare the rice pest impact coefficient in different monitoring areas with the preset pest impact coefficient threshold range, and determine whether pest control is needed based on the comparison results;

[0015] The in-depth verification module is used to analyze the changes in rice pests in different monitoring areas based on past data when it is determined that pest control operations are not necessary, and to further decide whether to carry out pest control operations based on the analysis results.

[0016] Furthermore, the information obtained by the drone shooting module includes the pest area, the insect egg area, the monitoring area, the rice planting density, the number of natural enemies of pests and the number of adult pests, and the information collected by the environmental information collection module includes the temperature, humidity and light intensity in the planting area.

[0017] Furthermore, the processing process of the data processing module includes:

[0018] By formula Calculate the environmental impact coefficient v at the i-th time point in the rice planting area i ;

[0019] Among them, i is a data collection at a fixed time interval, wd i is the temperature at the i-th time point in the rice planting area, wd y is the preset temperature, wd b wd i The standard value, sd i is the humidity at the i-th time point in the rice planting area, sd y is the preset humidity, sd b sd i The standard value of gx i is the light intensity at the i-th time point in the rice planting area, gx y is the preset light intensity, gx b For gx i The standard value of f c (x) is a defined function, if f c (x)≥1, then let f c (x) = (x), otherwise, let f c (x)=1.

[0020] Furthermore, the processing process of the data processing module also includes:

[0021] By formula Calculate the rice pest impact coefficient p at the i-th time point in the a-th monitoring area ai ;

[0022] Among them, a is any monitoring area divided by the regional division module, b is the total number of monitoring areas divided by the regional division module, ch ai is the pest area at the i-th time point in the a-th monitoring area, cl ai is the area of ​​eggs in the ath monitoring area at the i-th time point, x1 and x2 are the first weight coefficients, mj a is the total area of ​​the ath monitoring area, md a is the rice planting density in the ath monitoring area, md y is the preset rice planting density, td ai is the number of natural enemies of pests in the ath monitoring area at the i-th time point, dc ai is the number of adult pests in the a-th monitoring area at the i-th time point, and y1 and y2 are the second weight coefficients.

[0023] Furthermore, the analysis process of the data analysis module includes:

[0024] By combining the rice pest impact coefficient p at the i-th time point in all monitoring areas ai Compare with the preset pest impact coefficient threshold interval [p1, p2] respectively;

[0025] If all p ai If both are less than p1, the system determines that there is no serious insect pest in the rice planting area and no insecticide operation is needed, and further analysis is performed through the in-depth verification module;

[0026] If any p ai ∈[p1, p2], the system determines that the risk posed by rice pests in the area is low and the pest situation is mild, and performs pest control operations in the area by manually catching or cutting off the affected parts;

[0027] If any p ai If it is greater than p2, the system determines that the risk posed by rice pests in the area is high and the pest situation is serious, and it is necessary to spray pesticides to eliminate pests.

[0028] Furthermore, the verification process of the in-depth verification module includes:

[0029] By combining historical data, a real-time change curve p of the rice pest impact coefficient in the ath region is established. a(t);

[0030] And through the formula Calculate the change s of the rice pest impact coefficient in the ath region from time point t1 to time point t2;

[0031] Where g is the proportionality coefficient, For all p ai The maximum value in For all p ai The minimum value in For all p ai The average value of .

[0032] Furthermore, the verification process of the in-depth verification module also includes:

[0033] By comparing the change s of the rice pest impact coefficient in all areas from time point t1 to time point t2 with the preset change threshold s w Make a comparison;

[0034] If any s is greater than s w , the system judges that the change s of the rice pest impact coefficient in the area from time point t1 to time point t2 is large, indicating that the severity of the pest is gradually increasing, and it is necessary to promptly notify the back-end management personnel to carry out physical pest control operations;

[0035] If all s are less than s w , the system judges that the change s of the rice pest impact coefficient in all areas from time point t1 to time point t2 is small, indicating that the severity of the pest has decreased and no pest control operation is needed.

[0036] A rice planting area monitoring and management method based on unmanned aerial vehicle, the method comprising:

[0037] S1: The rice planting area is divided into multiple monitoring areas of the same size through the area division module;

[0038] S2: photographing rice image information in different areas through the photographing unit in the drone photographing module, identifying the rice graphic information photographed by the camera unit through the recognition unit, and saving the required data;

[0039] S3: Collect environmental data of the rice planting area at different time points through the environmental information collection module;

[0040] S4: Calculate the rice pest impact coefficient in different monitoring areas by combining the data from the drone shooting module with the data collected by the environment collection module through the data processing module;

[0041] S5: comparing the rice pest impact coefficients in different monitoring areas with the preset pest impact coefficient threshold ranges through the data analysis module, and judging whether pest control operations are required based on the comparison results;

[0042] S6: When it is determined that pest control is not necessary, the in-depth verification module is combined with past data to analyze the changes in rice pests in different monitoring areas, and a further decision on whether to carry out pest control is made based on the analysis results.

[0043] Beneficial effects of the present invention:

[0044] (1) The present invention calculates the rice pest impact coefficient in different monitoring areas by combining diversified data, and compares it with the preset pest impact coefficient threshold range, so as to accurately analyze the pest risk in different monitoring areas and judge whether it is necessary to use pesticides to eliminate the pests according to the level of pest risk, so as to avoid blindly using pesticides to eliminate the pests when the pests are in the early stage, resulting in pollution of crops, soil, water sources, etc.

[0045] (2) The present invention can reflect the severity of pests in different areas by combining the pest area, the number of eggs and the number of adults. Then, by combining the rice planting density, the environmental impact coefficient at the i-th time point and the number of natural enemies of pests, the living environment of pests can be reflected. Therefore, this calculation method can accurately reflect the risks brought by the current pests, thereby providing accurate and reliable data information for subsequent judgments on the risk level of pests, thereby ensuring the authenticity of the judgment results.

[0046] (3) The present invention calculates the rice pest impact coefficient p at the i-th time point in all monitoring areas. ai The preset threshold intervals [p1, p2] of the pest impact coefficient are compared respectively. By setting it in this way, it can be judged whether the area needs to be disinfested based on the comparison results, and different disinfestation plans can be formulated according to the size of the rice pest impact coefficient at the i-th time point in different monitoring areas, so as to avoid the situation where the crops, soil and water sources are polluted due to the blind use of pesticides to eliminate the pests in the early stages due to the inability to analyze the risks of the pests.

[0047] (4) The present invention compares the change s of the rice pest impact coefficient in all regions from time point t1 to time point t2 with the preset change threshold s. wBy comparing the data, we can evaluate and predict whether the severity of insect pests will increase in the future, and promptly notify the back-end management staff to carry out physical pest control operations, thereby realizing insect pest monitoring in rice-growing areas. Through such a setting, it can play an early warning role, thereby nipping in the bud the possible expansion of insect pests and avoiding affecting the growth of rice. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The present invention will be further described below in conjunction with the accompanying drawings.

[0049] Figure 1 It is a schematic block diagram of a rice planting area monitoring and management system based on drones in the present invention;

[0050] Figure 2 The present invention is a flow chart of a method for monitoring and managing a rice planting area based on an unmanned aerial vehicle. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] See also Figure 1 As shown, in one embodiment, the present application provides a rice planting area monitoring management system and method based on drones, the system comprising:

[0053] A region division module is used to divide the rice planting area into multiple monitoring areas of the same size;

[0054] The drone shooting module includes a camera unit and a recognition unit, wherein the camera unit is used to shoot rice image information in different areas;

[0055] The recognition unit is used to recognize the rice graphic information photographed by the camera unit and save the required data;

[0056] Environmental information collection module, used to collect environmental data of rice-growing areas at different time points;

[0057] A data processing module is used to calculate the impact coefficient of rice pests in different monitoring areas by combining the data collected by the drone shooting module with the data collected by the environment collection module;

[0058] The data analysis module is used to compare the rice pest impact coefficient in different monitoring areas with the preset pest impact coefficient threshold range, and determine whether pest control is needed based on the comparison results;

[0059] The in-depth verification module is used to analyze the changes in rice pests in different monitoring areas based on past data when it is determined that pest control is not necessary, and further decide whether to carry out pest control based on the analysis results;

[0060] Through the above technical scheme, this embodiment provides a region division module to divide the rice planting area into multiple monitoring areas of the same size. When monitoring the rice planting area, first, the camera unit in the drone shooting module is used to shoot rice image information in different areas, and the recognition unit is used to identify the rice graphic information shot by the camera unit, and the required data is saved. Then, the environmental data of the rice planting area at different time points is collected by the environmental information acquisition module, and the data processing module is used to combine the data of the drone shooting module with the data collected by the environmental acquisition module to calculate the rice pest impact coefficient in different monitoring areas. After that, the rice pest impact coefficient in different monitoring areas can be compared with the preset pest impact coefficient threshold interval through the data analysis module, and it is judged whether to perform pest control operations according to the comparison results. When it is judged that pest control operations are not needed, the change amount of rice pests in different monitoring areas can be analyzed by combining past data through the in-depth verification module, and further decision can be made whether to perform pest control operations according to the analysis results.

[0061] Through such a setting, by combining diversified data to calculate the rice pest impact coefficient in different monitoring areas, and comparing it with the preset pest impact coefficient threshold range, the pest risk in different monitoring areas can be accurately analyzed, and according to the level of pest risk, it can be judged whether it is necessary to use pesticides to eliminate pests, so as to avoid blindly using pesticides to eliminate pests when the pests are in the early stages, resulting in pollution of crops, soil, water sources, etc.

[0062] The information obtained by the drone photography module includes the pest area, the egg area, the monitoring area, the rice planting density, the number of natural enemies of pests and the number of adult pests. The information collected by the environmental information collection module includes the temperature, humidity and light intensity in the planting area.

[0063] Through the above technical scheme, this embodiment provides information obtained by the drone shooting module and information collected by the environmental information collection module, which can reflect the current insect pest situation in the rice planting area, thereby providing accurate data support for subsequent decisions on whether to use pesticides to eliminate pests, thereby improving the reliability and rationality of decision-making results, and avoiding the blind use of pesticides to eliminate pests, resulting in pollution of crops, soil, water sources, etc.

[0064] The processing process of the data processing module includes:

[0065] By formula Calculate the environmental impact coefficient v at the i-th time point in the rice planting area i ;

[0066] Among them, i is a data collection at a fixed time interval, wd i is the temperature at the i-th time point in the rice planting area, wd y is the preset temperature, wd b wd i The standard value can be selected and set according to the allowable error in the empirical data, sd i is the humidity at the i-th time point in the rice planting area, sd y is the preset humidity, sd b sd i The standard value of gx can be selected and set according to the allowable error in empirical data. i is the light intensity at the i-th time point in the rice planting area, gx y is the preset light intensity, gx b For gx i The standard value of f can be selected and set according to the allowable error in empirical data. c (x) is a defined function, if f c (x)≥1, then let f c (x) = (x), otherwise, let f c (x) = 1;

[0067] Through the above technical solution, this embodiment provides the environmental impact coefficient v at the i-th time point in the rice planting area i , can be obtained by formula It is calculated that, obviously, the greater the difference between the temperature and humidity at the i-th time point in the rice-growing area and the preset value, and the higher the light intensity, the greater the environmental impact coefficient at the i-th time point in the rice-growing area. This is because when the humidity and temperature are too high or too low, they are not suitable for the growth and reproduction of pests, and excessive light intensity will affect the growth of pests. Conversely, when the difference between the temperature and humidity at the i-th time point in the rice-growing area and the preset value is smaller, and the light intensity is lower, the smaller the environmental impact coefficient at the i-th time point in the rice-growing area. Through this calculation method, it can be reflected whether the current environment is suitable for the growth and reproduction of pests, thereby providing diversified data support for subsequent judgment of pest risks, thereby improving the reliability and accuracy of the data.

[0068] The processing process of the data processing module also includes:

[0069] By formula Calculate the rice pest impact coefficient p at the i-th time point in the a-th monitoring area ai ;

[0070] Among them, a is any monitoring area divided by the regional division module, b is the total number of monitoring areas divided by the regional division module, ch ai is the pest area at the i-th time point in the a-th monitoring area, cl ai is the area of ​​eggs in the ath monitoring area at the i-th time point, x1 and x2 are the first weight coefficients, which are set according to empirical fitting, and mj a is the total area of ​​the ath monitoring area, md a is the rice planting density in the ath monitoring area, md y is the preset rice planting density, td ai is the number of natural enemies of pests in the ath monitoring area at the i-th time point, dc ai is the number of adult pests in the a-th monitoring area at the i-th time point, y1 and y2 are the second weight coefficients, which are set according to empirical fitting;

[0071] Through the above technical solution, this embodiment provides the rice pest impact coefficient p at the i-th time point in the a-th monitoring area: ai , can be obtained by formula It is calculated that, obviously, when the pest area, egg area and rice planting density at the i-th time point in the a-th monitoring area are larger, and the number of natural enemies of pests is much less than the number of adults, then the rice pest impact coefficient at the i-th time point in the a-th monitoring area is larger, indicating that the pest risk at the current time point is greater; conversely, when the pest area, egg area and rice planting density at the i-th time point in the a-th monitoring area are smaller, and the number of natural enemies of pests is much greater than the number of adults, then the rice pest impact coefficient at the i-th time point in the a-th monitoring area is smaller. By combining the pest area, the number of eggs and the number of adults, the severity of pests in different areas can be reflected. Subsequently, by combining the rice planting density, the environmental impact coefficient at the i-th time point and the number of natural enemies of pests, the living environment of pests can be reflected. Therefore, this calculation method can accurately reflect the risks brought by the current pests, thereby providing accurate and reliable data information for subsequent judgments on the risk level of pests, thereby ensuring the authenticity of the judgment results.

[0072] The analysis process of the data analysis module includes:

[0073] By combining the rice pest impact coefficient p at the i-th time point in all monitoring areas ai Compare with the preset pest impact coefficient threshold interval [p1, p2] respectively;

[0074] If all p ai If both are less than p1, the system determines that there is no serious insect pest in the rice planting area and no insecticide operation is needed, and further analysis is performed through the in-depth verification module;

[0075] If any p ai ∈[p1, p2], the system determines that the risk posed by rice pests in the area is low and the pest situation is mild, and performs pest control operations in the area by manually catching or cutting off the affected parts;

[0076] If any p ai If it is greater than p2, the system determines that the risk posed by rice pests in the area is high and the pest situation is serious, and it is necessary to spray pesticides to eliminate the pests;

[0077] Through the above technical solution, this embodiment calculates the rice pest impact coefficient p at the i-th time point in all monitoring areas. aiCompare with the preset pest impact coefficient threshold interval [p1, p2] respectively. Through such setting, it can be judged whether the area needs to be disinfested according to the comparison result, and formulate different disinfestation plans according to the size of the rice pest impact coefficient at the i-th time point in different monitoring areas, so as to avoid the situation where the crops, soil and water sources are polluted due to the blind use of pesticides to eliminate the pests in the early stage due to the inability to analyze the risk of pests;

[0078] It should be noted that the above preset pest impact coefficient threshold interval [p1, p2] can be set based on empirical fitting.

[0079] The verification process of the in-depth verification module includes:

[0080] By combining historical data, a real-time change curve p of the rice pest impact coefficient in the ath region is established. a (t);

[0081] And through the formula Calculate the change s of the rice pest impact coefficient in the ath region from time point t1 to time point t2;

[0082] Among them, g is the proportionality coefficient, which is set according to empirical fitting. For all p ai The maximum value in For all p ai The minimum value in For all p ai The average value of

[0083] Through the above technical solution, this embodiment provides the change s of the rice pest impact coefficient in the ath region from time point t1 to time point t2, which can be expressed by the formula By this calculation method, the change s of the rice pest impact coefficient in the ath area from time point t1 to time point t2 can be calculated. This data can reflect the change of pest risk between time point t1 and time point t2. By combining this change, the future risk of pests can be predicted, so as to further analyze the risk of pests and decide whether to carry out pest control operations.

[0084] The verification process of the in-depth verification module also includes:

[0085] By comparing the change s of the rice pest impact coefficient in all areas from time point t1 to time point t2 with the preset change threshold s w Make a comparison;

[0086] If any s is greater than s w, the system judges that the change s of the rice pest impact coefficient in the area from time point t1 to time point t2 is large, indicating that the severity of the pest is gradually increasing, and it is necessary to promptly notify the back-end management personnel to carry out physical pest control operations;

[0087] If all s are less than s w , the system judges that the change s of the rice pest impact coefficient in all areas from time point t1 to time point t2 is small, indicating that the severity of the pest has decreased and no pest control operation is needed;

[0088] Through the above technical solution, this embodiment compares the change s of the rice pest impact coefficient in all regions from time point t1 to time point t2 with the preset change threshold s w By comparing the situation, we can evaluate and predict whether the severity of pests will increase in the future, and promptly notify the back-end management staff to carry out physical pest control operations, thereby realizing pest monitoring in rice planting areas. This setting can play an early warning role, thereby strangling the possible expansion of pests in the cradle and avoiding the impact on rice growth;

[0089] And the data is calculated based on the premise that there is no serious insect pest situation in the rice planting area, so only physical pest control operations are needed, and pesticides should not be used blindly to eliminate pests;

[0090] It should be noted that the above preset change threshold s w The setting can be fitted according to empirical data.

[0091] See also Figure 2 As shown, a rice planting area monitoring and management method based on drone, the method comprises:

[0092] S1: The rice planting area is divided into multiple monitoring areas of the same size through the area division module;

[0093] S2: photographing rice image information in different areas through the photographing unit in the drone photographing module, identifying the rice graphic information photographed by the camera unit through the recognition unit, and saving the required data;

[0094] S3: Collect environmental data of the rice planting area at different time points through the environmental information collection module;

[0095] S4: Calculate the rice pest impact coefficient in different monitoring areas by combining the data from the drone shooting module with the data collected by the environment collection module through the data processing module;

[0096] S5: comparing the rice pest impact coefficients in different monitoring areas with the preset pest impact coefficient threshold ranges through the data analysis module, and judging whether pest control operations are required based on the comparison results;

[0097] S6: When it is determined that pest control is not necessary, the in-depth verification module is used to analyze the changes in rice pests in different monitoring areas in combination with past data, and a decision is made on whether to carry out pest control based on the analysis results;

[0098] Through the above technical scheme, this embodiment provides a rice planting area monitoring and management method based on drones. First, the rice planting area is divided into multiple monitoring areas of the same size through the area division module. When monitoring, the rice image information in different areas is photographed by the shooting unit in the drone shooting module, and the rice graphic information photographed by the camera unit is identified by the recognition unit, and the required data is saved, and the environmental data of the rice planting area at different time points is collected by the environmental information acquisition module. Then, the data processing module combines the data of the drone shooting module with the data collected by the environmental acquisition module to calculate the rice pest impact coefficient in different monitoring areas, and the data analysis module compares the rice pest impact coefficient in different monitoring areas with the preset pest impact coefficient threshold interval, and judges whether to perform pest control operations according to the comparison results. Finally, when it is judged that pest control operations are not needed, the in-depth verification module is combined with past data to analyze the change amount of rice pests in different monitoring areas, and further decides whether to perform pest control operations according to the analysis results;

[0099] Through such settings, first, the data from the drone shooting module and the data collected by the environment collection module are combined to calculate the rice pest impact coefficient in different monitoring areas, and combined with the preset pest impact coefficient threshold range for analysis, it is possible to make a judgment on whether pest control is needed in different monitoring areas. Subsequently, different pest control plans can be formulated by combining the rice pest impact coefficients in different monitoring areas to avoid blindly using pesticides to eliminate pests, which leads to pollution of crops, soil, water sources, etc. After that, the in-depth verification module is used to analyze the change in rice pests in different monitoring areas, and further decisions can be made on whether to carry out pest control operations, thereby strangling the pests in the cradle.

[0100] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A rice planting area monitoring and management system based on drones, characterized in that: The system comprises: A region division module is used to divide the rice planting area into multiple monitoring areas of the same size; The drone shooting module includes a camera unit and a recognition unit, wherein the camera unit is used to shoot rice image information in different areas; The recognition unit is used to recognize the rice graphic information photographed by the camera unit and save the required data; Environmental information collection module, used to collect environmental data of rice-growing areas at different time points; A data processing module is used to calculate the impact coefficient of rice pests in different monitoring areas by combining the data collected by the drone shooting module with the data collected by the environment collection module; The data analysis module is used to compare the rice pest impact coefficient in different monitoring areas with the preset pest impact coefficient threshold range, and determine whether pest control is needed based on the comparison results; The in-depth verification module is used to analyze the changes in rice pests in different monitoring areas based on past data when it is determined that pest control operations are not necessary, and to further decide whether to carry out pest control operations based on the analysis results.

2. The rice planting area monitoring and management system based on drone according to claim 1 is characterized in that: The information obtained by the drone shooting module includes the pest area, the insect egg area, the monitoring area, the rice planting density, the number of natural enemies of pests and the number of adult pests. The information collected by the environmental information collection module includes the temperature, humidity and light intensity in the planting area.

3. The rice planting area monitoring and management system based on drone according to claim 2 is characterized in that: The processing process of the data processing module includes: By formula Calculate the environmental impact coefficient v at the i-th time point in the rice planting area i ; Among them, i is a data collection at a fixed time interval, wd i is the temperature at the i-th time point in the rice planting area, wd y is the preset temperature, wd b wd i The standard value, sd i is the humidity at the i-th time point in the rice planting area, sd y is the preset humidity, sd b sd i The standard value of gx i is the light intensity at the i-th time point in the rice planting area, gx y is the preset light intensity, gx b For gx i The standard value, f c (x) is a defined function, if f c (x)≥1, then let f c (x) = (x), otherwise, let f c (x)=1.

4. The rice planting area monitoring and management system based on drone according to claim 3 is characterized in that: The processing process of the data processing module also includes: By formula Calculate the rice pest impact coefficient p at the i-th time point in the a-th monitoring area ai ; Among them, a is any monitoring area divided by the regional division module, b is the total number of monitoring areas divided by the regional division module, ch ai is the pest area at the i-th time point in the a-th monitoring area, cl ai is the area of ​​eggs in the ath monitoring area at the i-th time point, x1 and x2 are the first weight coefficients, mj a is the total area of ​​the ath monitoring area, md a is the rice planting density in the ath monitoring area, md y is the preset rice planting density, td ai is the number of natural enemies of pests in the ath monitoring area at the i-th time point, dc ai is the number of adult pests in the a-th monitoring area at the i-th time point, and y1 and y2 are the second weight coefficients.

5. The rice planting area monitoring and management system based on drone according to claim 4 is characterized in that: The analysis process of the data analysis module includes: By combining the rice pest impact coefficient p at the i-th time point in all monitoring areas ai Compare with the preset pest impact coefficient threshold interval [p1, p2] respectively; If all p ai If both are less than p1, the system determines that there is no serious insect pest in the rice planting area and no insecticide operation is needed, and further analysis is performed through the in-depth verification module; If any p ai ∈[p1, p2], the system determines that the risk posed by rice pests in the area is low and the pest situation is mild, and performs pest control operations in the area by manually catching or cutting off the affected parts; If any p ai If it is greater than p2, the system determines that the risk posed by rice pests in the area is high and the pest situation is serious, and it is necessary to spray pesticides to eliminate pests.

6. The rice planting area monitoring and management system based on drone according to claim 5 is characterized in that: The verification process of the in-depth verification module includes: By combining historical data, a real-time change curve p of the rice pest impact coefficient in the ath region is established. a (t); And through the formula Calculate the change s of the rice pest impact coefficient in the ath region from time point t1 to time point t2; Where g is the proportionality coefficient, For all p ai The maximum value in For all p ai The minimum value in For all p ai The average value of .

7. The rice planting area monitoring and management system based on drone according to claim 6 is characterized in that: The verification process of the in-depth verification module also includes: By comparing the change s of the rice pest impact coefficient in all areas from time point t1 to time point t2 with the preset change threshold s w Make a comparison; If any s is greater than s w , the system judges that the change s of the rice pest impact coefficient in the area from time point t1 to time point t2 is large, indicating that the severity of the pest is gradually increasing, and it is necessary to promptly notify the back-end management personnel to carry out physical pest control operations; If all s are less than s w , the system judges that the change s of the rice pest impact coefficient in all areas from time point t1 to time point t2 is small, indicating that the severity of the pest has decreased and no pest control operation is needed.

8. A rice planting area monitoring and management method based on drones, characterized in that: The method adopts a rice planting area monitoring and management system based on a drone as described in claims 1-7, and the method comprises: S1: The rice planting area is divided into multiple monitoring areas of the same size through the area division module; S2: photographing rice image information in different areas through the photographing unit in the drone photographing module, identifying the rice graphic information photographed by the camera unit through the recognition unit, and saving the required data; S3: Collect environmental data of the rice planting area at different time points through the environmental information collection module; S4: Calculate the rice pest impact coefficient in different monitoring areas by combining the data from the drone shooting module with the data collected by the environment collection module through the data processing module; S5: comparing the rice pest impact coefficients in different monitoring areas with the preset pest impact coefficient threshold ranges through the data analysis module, and judging whether pest control operations are required based on the comparison results; S6: When it is determined that pest control is not necessary, the in-depth verification module is combined with past data to analyze the changes in rice pests in different monitoring areas, and a further decision on whether to carry out pest control is made based on the analysis results.

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