Farming land state detail surveying and mapping method and system based on Internet of Things

Abstract

The invention relates to the technical field of farming land surveying and mapping, and particularly discloses a farming land state detail surveying and mapping method and system based on the Internet of Things, and an environment evaluation module which is used for collecting shooting environment data at different time points and evaluating whether a good shooting environment exists at the current time point according to environment data information. Whether a good shooting environment exists at the current time point or not is evaluated in combination with the environment data, it can be guaranteed that the unmanned aerial vehicle shoots and maps the images of the farming land in the good shooting environment, then the farming land images shot by the unmanned aerial vehicle are evaluated, whether the shot farming land images are qualified or not can be judged, and the accuracy of the farming land images is improved. Through the arrangement, the influence of the external environment can be eliminated, the shooting effect can be improved, and the quality of the farming land image can be further improved by judging whether the quality of the farming land image is qualified or not, so that data in the image can be accurately identified, and the accuracy of a surveying and mapping result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural land surveying and mapping, and in particular to a method and system for surveying and mapping agricultural land status details based on the Internet of Things. Background Art

[0002] Detailed mapping of agricultural land status integrates multiple technologies and equipment to provide farmers and agricultural managers with comprehensive, accurate and real-time land status information, so as to formulate more reasonable agricultural management measures such as irrigation, fertilization, pest and disease control, improve agricultural production efficiency and reduce the impact on the environment.

[0003] The most common method of agricultural land mapping is drone mapping, which uses drones to obtain images and data of farmland through aerial photography, and then extracts useful information about the farmland through image processing and analysis. After obtaining the data, the staff can analyze the nutritional status of the agricultural land based on the data, and judge whether fertilization is needed to ensure sufficient nutrition of the land based on the analysis results, and formulate more reasonable fertilization management measures based on the judgment results, so as to ensure a good growth environment for crops.

[0004] In the prior art, the quality of images taken by drones is particularly important when surveying and mapping agricultural land. However, drone photography is affected by the external environment and the operating status of the equipment, resulting in poor shooting effects. On this basis, the data in the image cannot be accurately identified, resulting in inaccurate surveying and mapping results, affecting the accuracy of the nutritional status analysis results of the agricultural land. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for mapping the details of agricultural land status based on the Internet of Things to solve the following technical problems:

[0006] How to improve the accuracy of agricultural land mapping and nutritional status analysis of agricultural land.

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

[0008] A method and system for mapping details of agricultural land status based on the Internet of Things, the system comprising:

[0009] The environment assessment module is used to collect shooting environment data at different time points and evaluate whether there is a good shooting environment at the current time point based on the environment data information;

[0010] The drone camera module is used to shoot and map images of farmland through drones when it is determined that there is a good shooting environment at the current time point;

[0011] The surveying and mapping evaluation module is used to evaluate the agricultural land images taken by the drone and determine whether the agricultural land images taken by the drone are qualified based on the evaluation results;

[0012] The recognition and analysis module is used to identify the features in the image when judging whether the image of the cultivated land taken by the drone is qualified, and to analyze and evaluate the state of the cultivated land based on the data.

[0013] Furthermore, the information collected by the environmental assessment module includes: light intensity, light angle, suspended matter content in the air, temperature and wind speed at different time points, and the image features identified in the recognition and analysis module include the color, smoothness, plant height, leaf area, number of insect spots and area of ​​insect spots of crops.

[0014] Furthermore, the evaluation process of the environmental evaluation module includes:

[0015] By formula Calculate the environmental impact coefficient k at the i-th time point i ;

[0016] Where i is any time point, gq i is the light intensity at the i-th time point, qg y is the preset light intensity, gq b For gq i The standard value of gj i is the light irradiation angle at the i-th time point, gj y is the preset irradiation angle, gj b For gj i The standard value, wd i is the outdoor temperature at the i-th time point, jw i is the internal temperature of the drone at the i-th time point, wdc y is the preset temperature difference, f c (x) is a defined function, if f c (x)≥1, then let f c (x) = (x), otherwise, let f c (x) = 1, fs i is the outdoor wind speed at the i-th time point, fs y is the preset wind speed, xf i is the suspended matter content in the air at the i-th time point, xf y is the preset suspended matter content, ρ is the error correction coefficient, which is set based on empirical fitting.

[0017] Furthermore, the assessment process of the environmental assessment module also includes:

[0018] By taking the environmental impact coefficient k at the i-th time pointi The preset environmental impact coefficient threshold k x Make a comparison;

[0019] If k i ≥k x ,The system determines that the environment at the current time point has a high impact on the shooting quality and is not suitable for image acquisition operations, and continues to perform environmental assessment operations;

[0020] If k i <k x ,The system determines that the environment at the current time point has a low impact on the ,photographing quality, the system can use the drone to take images of the ,cultivated land and stop the environmental assessment operation.

[0021] Furthermore, the evaluation process of the surveying and mapping evaluation module includes:

[0022] First, the drone takes images of the farmland when it is determined that the environment at the current time point has a low impact on the shooting quality;

[0023] Then through the formula Calculate and obtain the qualified quality index s of the agricultural land image;

[0024] Among them, t is the image of any area taken by the drone, g is the total number of areas taken, and moh t is the area of ​​the virtual shadow in the t-th region image, moh y is the preset virtual shadow area, qxd t is the clarity of the image in the tth region, qxd y is the preset clarity, a is any scoring expert, b is the total number of experts, pf at is the quality score of the image of the tth region given by the scoring expert a, x a is the weight coefficient of the ath scoring expert;

[0025] Then, the agricultural land image quality qualification index s is compared with the preset quality qualification index threshold s x Make a comparison;

[0026] If x ,The system judged that the image quality of agricultural land was unqualified, the surveying and mapping results were inaccurate, and rescheduled the shooting;

[0027] If s≥s x ,The system judges that the quality of the agricultural land image is qualified, the ,surveying results are highly accurate, and analyzes and evaluates the ,status of agricultural land through the recognition and analysis module.

[0028] Furthermore, the analysis process of the identification and analysis module includes:

[0029] ​First, the required features in the images of agricultural land taken by drones are identified and recorded;

[0030] And through the formula Calculate the pest impact coefficient in agricultural land;

[0031] Then through the formula Calculate the growth coefficient δ of crops in the agricultural land;

[0032] Among them, cb t is the number of worm spots in the t-th region image, cb y is the preset number of insect spots, cm t is the area of ​​the worm spot in the t-th region image, cb y is the preset insect spot area, ysld t is the color brightness of crops in the t-th region image, ysld y is the preset color brightness, pz t is the smoothness of the crop in the t-th region image, pz y is the preset smoothness, is the plant height change curve of crops in the t-th regional image, which is established by combining historical monitoring data, β t (x) is the leaf size change curve of the crop in the t-th region image, which is established by combining historical monitoring data. r1 and r2 are the starting time point in historical monitoring and the time point of the current detection, respectively.

[0033] Furthermore, the analysis process of the identification and analysis module also includes:

[0034] By comparing the growth coefficient δ of crops in the cultivated land with the preset growth coefficient threshold δ x Make a comparison;

[0035] If δ<δ x , the system judges that the growth of crops in the farmland is slow, which reflects the lack of nutrition in the current farmland, and it is necessary to apply fertilizer in time to ensure that the land is fully nutritious;

[0036] If δ ≥ δ x ,The system determines that the crops in the agricultural land grow fast, which reflects that the nutrition in the current agricultural land is sufficient and there is no need to apply fertilizer to ensure that the land is fully nutritious.

[0037] A method for surveying and mapping details of cultivated land status based on the Internet of Things, the method comprising:

[0038] S1: Collect shooting environment data at different time points through the environment assessment module, and evaluate whether there is a good shooting environment at the current time point based on the environment data information;

[0039] S2: When it is determined that there is a good shooting environment at the current time point, the image of the farmland is photographed and mapped by a drone;

[0040] S3: Evaluate the agricultural land images taken by the drone through the surveying and mapping evaluation module, and determine whether the agricultural land images taken by the drone are qualified according to the evaluation results;

[0041] S4: When the image of the cultivated land taken by the UAV is judged to be qualified, the recognition and analysis module is used to identify the features in the image, and the state of the cultivated land is analyzed and evaluated in combination with the data.

[0042] Beneficial effects of the present invention:

[0043] (1) The present invention evaluates whether there is a good shooting environment at the current time point in combination with environmental data, thereby ensuring that the drone can shoot and map images of cultivated land in a good shooting environment. After that, by evaluating the images of cultivated land taken by the drone, it is also possible to judge whether the images of cultivated land taken are qualified. Through such a setting, not only can the influence of the external environment be eliminated to improve the shooting effect, but also the quality of the images of cultivated land can be further improved by judging whether the quality of the images of cultivated land is qualified, thereby facilitating accurate identification of data in the image and improving the accuracy of the mapping results.

[0044] (2) The present invention calculates the environmental impact coefficient k at the i-th time point i The preset environmental impact coefficient threshold k x Through this comparison method, we can make an accurate judgment on the impact of the current time point environment on the shooting quality, and further analyze whether it is suitable for image acquisition operations. Through such settings, we can ensure that when the drone takes images of cultivated land, the environment has little impact on the shooting quality, thereby ensuring the quality of the captured images and improving the accuracy of subsequent surveying and mapping results.

[0045] (3) The present invention compares the agricultural land image quality qualification index s with the preset quality qualification index threshold s x By comparing, we can make an accurate judgment on whether the quality of the cultivated land images is qualified, and whether the quality of the cultivated land images is qualified represents the accuracy of the surveying and mapping results. Therefore, through this comparison method, the staff can make a judgment on the surveying and mapping results of the cultivated land images based on the judgment results, and select the cultivated land images with qualified quality as the surveying and mapping results to ensure the accuracy of the surveying and mapping results, thereby improving the accuracy of the nutritional status analysis results of the cultivated land.

[0046] (4) The present invention compares the growth coefficient δ of crops in the cultivated land with the preset growth coefficient threshold δ xBy comparing, we can analyze the growth speed of crops in the cultivated land, and analyze the nutritional status of the current cultivated land based on the analysis results. Since the accuracy of the surveying and mapping results is relatively high, the accuracy of the analysis results of the nutritional status of the current cultivated land can be improved, ensuring that the staff can make accurate fertilization decisions based on the analysis results, thereby ensuring that the land is adequately nutritious, and formulate more reasonable fertilization management measures, thereby ensuring that crops have a good growth environment.

[0047] (5) The present invention uses an environmental assessment module to assess whether there is a good shooting environment at the current time point, which can ensure that the drone can perform shooting operations without external interference, thereby ensuring the quality of the images it captures. Thereafter, by evaluating the captured agricultural land images, it can ensure that the highest quality agricultural land images are obtained, thereby improving the accuracy of the surveying and mapping results. Moreover, when analyzing the status of the agricultural land based on the highly accurate surveying and mapping results, the accuracy and reliability of the analysis results can be guaranteed, thereby improving the accuracy of agricultural land surveying and mapping and the analysis of the nutritional status of the agricultural land. 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 detailed agricultural land status surveying and mapping system based on the Internet of Things in the present invention;

[0050] Figure 2 It is a flow chart of a method for surveying and mapping details of agricultural land status based on the Internet of Things in the present invention; 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 method and system for mapping details of agricultural land status based on the Internet of Things, the system comprising:

[0053] The environment assessment module is used to collect shooting environment data at different time points and evaluate whether there is a good shooting environment at the current time point based on the environment data information;

[0054] The drone camera module is used to shoot and map images of farmland through drones when it is determined that there is a good shooting environment at the current time point;

[0055] The surveying and mapping evaluation module is used to evaluate the agricultural land images taken by the drone and determine whether the agricultural land images taken by the drone are qualified based on the evaluation results;

[0056] The recognition and analysis module is used to identify the features in the image when judging whether the image of the cultivated land taken by the drone is qualified, and analyze and evaluate the state of the cultivated land based on the data;

[0057] Through the above technical solution, this embodiment provides an environmental assessment module, which is used to collect shooting environment data at different time points, and evaluate whether there is a good shooting environment at the current time point based on the environmental data information. After that, when it is judged that there is a good shooting environment at the current time point, the image of the cultivated land is photographed and mapped by the drone, and the agricultural land image photographed by the drone is evaluated by the mapping assessment module, and whether the agricultural land image photographed by the drone is qualified is judged according to the evaluation result. Finally, when it is judged that the agricultural land image photographed by the drone is qualified, the features in the image can be identified by the recognition and analysis module, and the agricultural land status is analyzed and evaluated in combination with the data;

[0058] By such a setting, the system can evaluate whether there is a good shooting environment at the current time point by combining environmental data, so as to ensure that the drone can shoot and map the images of the cultivated land in a good shooting environment. After that, by evaluating the images of the cultivated land taken by the drone, it can also be judged whether the images of the cultivated land taken are qualified. By such a setting, not only can the influence of the external environment be eliminated to improve the shooting effect, but also the quality of the images of the cultivated land can be further improved by judging whether the quality of the images of the cultivated land is qualified, so as to facilitate accurate recognition of the data in the images and improve the accuracy of the mapping results.

[0059] After obtaining the surveying image of the cultivated land, the features in the image are identified through the recognition and analysis module. High-quality surveying images can provide accurate data. Combined with accurate and reliable surveying images, the status of the cultivated land is analyzed and evaluated, which can greatly improve the accuracy of the nutritional status analysis results of the cultivated land. The staff can accurately judge whether fertilization is needed based on the analysis results to ensure that the land is adequately nutritious. Based on the data, the staff can formulate more reasonable fertilization management measures to ensure a good growth environment for crops.

[0060] The information collected by the environmental assessment module includes: light intensity, light angle, suspended matter content in the air, temperature and wind speed at different time points; the image features identified in the recognition and analysis module include the color, smoothness, plant height, leaf area, number of insect spots and area of ​​insect spots of crops;

[0061] Through the above technical solution, this embodiment provides information collected by the environmental assessment module, and the data information can reflect the current shooting environment. By combining the data, it is possible to analyze and judge whether the shooting quality of the drone in the current environment is affected, so that the drone can work under the condition of less environmental impact, thereby ensuring the shooting quality and further ensuring the accuracy of subsequent surveying and mapping results;

[0062] The image features identified in the recognition and analysis module can reflect the current growth status of crops in the cultivated land. This data can indirectly reflect the fertility and nutritional status of the cultivated land. This information helps the staff to evaluate the impact status of the land and formulate reasonable fertilization measures to ensure a good growth environment for crops.

[0063] The assessment process of the environmental assessment module includes:

[0064] By formula Calculate the environmental impact coefficient k at the i-th time point i ;

[0065] Where i is any time point, gq i is the light intensity at the i-th time point, qg y is the preset light intensity, gq b For gq i The standard value of gj can be selected and set according to the allowable error in the empirical data. i is the light irradiation angle at the i-th time point, gj y is the preset irradiation angle, gj b For gj i The standard value of wd can be set based on the allowable error in the empirical data. i is the outdoor temperature at the i-th time point, jw i is the internal temperature of the drone at the i-th time point, wdc y is the preset temperature difference, f c (x) is a defined function, if f c (x)≥1, then let f c (x) = (x), otherwise, let f c (x) = 1, fs i is the outdoor wind speed at the i-th time point, fs y is the preset wind speed, xfi is the suspended matter content in the air at the i-th time point, xf y is the preset suspended solids content, ρ is the error correction coefficient, which is set based on empirical fitting;

[0066] Through the above technical solution, this embodiment provides the environmental impact coefficient k at the i-th time point: i , can be obtained by formula Obviously, when the light intensity and light angle at the i-th time point differ from the preset value, the internal temperature of the drone is higher than the outdoor temperature, and the wind speed and the content of suspended matter in the air are higher, then the environmental impact coefficient k at the i-th time point is i The larger the value, the greater the difference between the light intensity and the light angle and the preset value. The captured image will appear dim or overexposed. The higher the internal temperature of the drone is than the outdoor temperature, the more likely it is that fog will form on the lens and affect the clarity of the shot. The higher the wind speed and the content of suspended matter in the air will affect the stability of the drone's shooting and the clarity of the shot.

[0067] Therefore, when the light intensity and light irradiation angle at the i-th time point are smaller than the preset values, the internal temperature of the drone is closer to the outdoor temperature, and the wind speed and the content of suspended matter in the air are smaller, then the environmental impact coefficient k at the i-th time point is i The smaller it is, the more it affects the image quality of the drone. This calculation method can reflect the impact of the current environment on the image quality of the drone. The staff can use the data to select the appropriate time to use the drone to shoot the farmland, avoiding the situation where the environmental factors are too influential and the drone's shooting effect deteriorates.

[0068] The assessment process of the environmental assessment module also includes:

[0069] By taking the environmental impact coefficient k at the i-th time point i The preset environmental impact coefficient threshold k x Make a comparison;

[0070] If k i ≥k x ,The system determines that the environment at the current time point has a high impact on the shooting quality and is not suitable for image acquisition operations, and continues to perform environmental assessment operations;

[0071] If k i <k x ,The system determines that the environment at the current time point has a low impact on the shooting quality, and can use the drone to shoot images of the cultivated land and stop the environmental assessment operation;

[0072] Through the above technical solution, this implementation calculates the environmental impact coefficient k at the i-th time pointi The preset environmental impact coefficient threshold k x Through this comparison method, we can make an accurate judgment on the impact of the current time point environment on the shooting quality, and further analyze whether it is suitable for image acquisition operations. Through such settings, we can ensure that when the drone takes images of cultivated land, the environment has little impact on the shooting quality, thereby ensuring the quality of the captured images and improving the accuracy of subsequent surveying and mapping results.

[0073] The evaluation process of the surveying and mapping evaluation module includes:

[0074] First, the drone takes images of the farmland when it is determined that the environment at the current time point has a low impact on the shooting quality;

[0075] Then through the formula Calculate and obtain the qualified quality index s of the agricultural land image;

[0076] Among them, t is the image of any area taken by the drone, g is the total number of areas taken, and moh t is the area of ​​the virtual shadow in the t-th region image, moh y is the preset virtual shadow area, qxd t is the clarity of the image in the tth region, qxd y is the preset clarity, a is any scoring expert, b is the total number of experts, pf at is the quality score of the image of the tth region given by the scoring expert a, x a is the weight coefficient of the ath scoring expert;

[0077] Then, the agricultural land image quality qualification index s is compared with the preset quality qualification index threshold s x Make a comparison;

[0078] If x ,The system judged that the image quality of agricultural land was unqualified, the surveying and mapping results were inaccurate, and rescheduled the shooting;

[0079] If s≥s x ,The system judges that the image quality of agricultural land is qualified, the mapping results are accurate, and analyzes and evaluates the state of agricultural land through the recognition and analysis module;

[0080] Through the above technical solution, the quality index s of the cultivated land image is provided, which can be obtained by the formula ​It is obvious that when the area of ​​the virtual shadow in the t-th region image is smaller and the clarity is higher, and the quality score of the scoring expert is higher, then the qualified index s of the agricultural land image quality is higher; conversely, when the area of ​​the virtual shadow in the t-th region image is larger and the clarity is lower, and the quality score of the scoring expert is lower, then the qualified index s of the agricultural land image quality is lower. Through this calculation method, the quality of the agricultural land image can be judged;

[0081] Then, the agricultural land image quality qualification index s is compared with the preset quality qualification index threshold s x By comparing, we can make an accurate judgment on whether the quality of the cultivated land images is qualified, and whether the quality of the cultivated land images is qualified represents the accuracy of the surveying and mapping results. Therefore, through this comparison method, the staff can make a judgment on the surveying and mapping results of the cultivated land images based on the judgment results, and select the cultivated land images with qualified quality as the surveying and mapping results to ensure the accuracy of the surveying and mapping results, thereby improving the accuracy of the nutritional status analysis results of the cultivated land.

[0082] The analysis process of the identification and analysis module includes:

[0083] First, the required features in the images of agricultural land taken by drones are identified and recorded;

[0084] And through the formula Calculate the pest impact coefficient in agricultural land;

[0085] Then through the formula Calculate the growth coefficient δ of crops in the agricultural land;

[0086] Among them, cb t is the number of worm spots in the t-th region image, cb y is the preset number of insect spots, cm t is the area of ​​the worm spot in the t-th region image, cb y is the preset insect spot area, ysld t is the color brightness of crops in the t-th region image, ysld y is the preset color brightness, pz t is the smoothness of the crop in the t-th region image, pz y is the preset smoothness, is the plant height change curve of crops in the t-th regional image, which is established by combining historical monitoring data, β t (x) is the leaf size change curve of the crop in the t-th region image, which is established by combining historical monitoring data. r1 and r2 are the starting time point in historical monitoring and the time point of the current detection, respectively;

[0087] Through the above technical solution, this embodiment provides the growth coefficient δ of crops in agricultural land, which can be first calculated by the formula Calculate the pest impact coefficient in agricultural land and use the formula Calculate and obtain, where and The two sets of calculation formulas can respectively calculate the changes in the plant height and leaf area of ​​crops in the t-th regional image from the beginning of historical monitoring to the present. Combining the formulas, when the color brightness and smoothness of the crops in the t-th regional image are higher, the changes in the plant height and leaf area of ​​crops from the beginning of historical monitoring to the present are greater, and the smaller the pest impact coefficient in the cultivated land, the greater the growth coefficient δ of the crops in the cultivated land;

[0088] On the contrary, when the color brightness and smoothness of the crops in the t-th area image are lower, the changes in the plant height and leaf area of ​​the crops from the beginning of historical monitoring to the present are smaller, and the pest impact coefficient in the cultivated land is greater, then the growth coefficient δ of the crops in the cultivated land is smaller. Through this calculation method, the growth trend of crops can be analyzed. The data can reflect the nutritional status of the cultivated land from the side, thereby providing accurate data for the staff to analyze whether fertilization is needed to ensure sufficient nutrition of the land.

[0089] The analysis process of the identification and analysis module also includes:

[0090] By comparing the growth coefficient δ of crops in the cultivated land with the preset growth coefficient threshold δ x Make a comparison;

[0091] If δ<δ x , the system judges that the growth of crops in the farmland is slow, which reflects the lack of nutrition in the current farmland, and it is necessary to apply fertilizer in time to ensure that the land is fully nutritious;

[0092] If δ ≥ δ x ,The system judges that the crops in the farmland grow fast, which reflects that the nutrition in the current farmland is sufficient and there is no need to apply fertilizer to ensure the nutrition of the land;

[0093] Through the above technical solution, this embodiment compares the growth coefficient δ of crops in the cultivated land with the preset growth coefficient threshold δ xBy comparing, we can analyze the growth speed of crops in the cultivated land, and analyze the nutritional status of the current cultivated land based on the analysis results. Since the accuracy of the surveying and mapping results is relatively high, the accuracy of the analysis results of the nutritional status of the current cultivated land can be improved, ensuring that the staff can make accurate fertilization decisions based on the analysis results, thereby ensuring that the land is adequately nutritious, and formulate more reasonable fertilization management measures, thereby ensuring that crops have a good growth environment.

[0094] See also Figure 2 As shown, a method for mapping the details of agricultural land status based on the Internet of Things, the method comprising:

[0095] S1: Collect shooting environment data at different time points through the environment assessment module, and evaluate whether there is a good shooting environment at the current time point based on the environment data information;

[0096] S2: When it is determined that there is a good shooting environment at the current time point, the image of the farmland is photographed and mapped by a drone;

[0097] S3: Evaluate the agricultural land images taken by the drone through the surveying and mapping evaluation module, and determine whether the agricultural land images taken by the drone are qualified according to the evaluation results;

[0098] S4: When the image of the cultivated land taken by the drone is judged to be qualified, the recognition and analysis module identifies the features in the image, and analyzes and evaluates the state of the cultivated land in combination with the data;

[0099] Through the above technical scheme, this embodiment provides a method for mapping the details of the state of cultivated land based on the Internet of Things. First, the shooting environment data at different time points are collected through the environmental assessment module, and whether there is a good shooting environment at the current time point is evaluated according to the environmental data information. When it is judged that there is a good shooting environment at the current time point, the image of the cultivated land is photographed and mapped by the drone, and the agricultural land image photographed by the drone is evaluated through the mapping assessment module, and whether the agricultural land image photographed by the drone is qualified is judged according to the evaluation result. When it is judged that the agricultural land image photographed by the drone is qualified, the features in the image are identified through the recognition and analysis module, and the state of the cultivated land is analyzed and evaluated in combination with the data;

[0100] Through such settings, the system evaluates whether there is a good shooting environment at the current time point through the environmental assessment module, which can ensure that the drone can perform shooting operations without external interference, thereby ensuring the quality of its captured images. After that, by evaluating the captured agricultural land images, it can ensure that the highest quality agricultural land images are obtained, thereby improving the accuracy of the surveying and mapping results, and when analyzing the status of agricultural land based on highly accurate surveying and mapping results, the accuracy and reliability of the analysis results can be guaranteed, thereby improving the accuracy of agricultural land surveying and mapping and the nutritional status analysis of agricultural land.

[0101] 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 detailed surveying and mapping system of agricultural land status based on the Internet of Things, characterized in that: The system comprises: The environment assessment module is used to collect shooting environment data at different time points and evaluate whether there is a good shooting environment at the current time point based on the environment data information; The drone camera module is used to shoot and map images of farmland through drones when it is determined that there is a good shooting environment at the current time point; The surveying and mapping evaluation module is used to evaluate the agricultural land images taken by the drone and determine whether the agricultural land images taken by the drone are qualified based on the evaluation results; The recognition and analysis module is used to identify the features in the image when judging whether the image of the cultivated land taken by the drone is qualified, and to analyze and evaluate the state of the cultivated land based on the data.

2. The agricultural land status details mapping system based on the Internet of Things according to claim 1 is characterized in that: The information collected by the environmental assessment module includes: light intensity, light angle, suspended matter content in the air, temperature and wind speed at different time points. The image features identified in the recognition and analysis module include the color, smoothness, plant height, leaf area, number of insect spots and area of ​​insect spots of crops.

3. The agricultural land status details mapping system based on the Internet of Things according to claim 2 is characterized in that: The assessment process of the environmental assessment module includes: By formula Calculate the environmental impact coefficient k at the i-th time point i ; Where i is any time point, gq i is the light intensity at the i-th time point, qg y is the preset light intensity, gq b For gq i The standard value of gj i is the light irradiation angle at the i-th time point, gj y is the preset irradiation angle, gj b For gj i The standard value, wd i is the outdoor temperature at the i-th time point, jw i is the internal temperature of the drone at the i-th time point, wdc y is the preset temperature difference, f c (x) is a defined function, if f c (x)≥1, then let f c (x) = (x), otherwise, let f c (x) = 1, fs i is the outdoor wind speed at the i-th time point, fs y is the preset wind speed, xf i is the suspended matter content in the air at the i-th time point, xf y is the preset suspended matter content, ρ is the error correction coefficient, which is set based on empirical fitting.

4. The agricultural land status detailed mapping system based on the Internet of Things according to claim 3 is characterized in that: The assessment process of the environmental assessment module also includes: By taking the environmental impact coefficient k at the i-th time point i and the preset environmental impact coefficient threshold k x Make a comparison; If k i ≥k x ,The system determines that the environment at the current time point has a high impact on the shooting quality and is not suitable for image acquisition operations, and continues to perform environmental assessment operations; If k i <k x ,The system determines that the environment at the current time point has a low impact on the ,photographing quality, the system can use the drone to take images of the ,cultivated land and stop the environmental assessment operation.

5. The agricultural land status detailed mapping system based on the Internet of Things according to claim 4 is characterized in that: The evaluation process of the surveying and mapping evaluation module includes: First, the drone takes images of the farmland when it is determined that the environment at the current time point has a low impact on the shooting quality; Then through the formula Calculate and obtain the qualified quality index s of the agricultural land image; Among them, t is the image of any area taken by the drone, g is the total number of areas taken, and moh t is the area of ​​the virtual shadow in the t-th region image, moh y is the preset virtual shadow area, qxd t is the clarity of the image in the tth region, qxd y is the preset clarity, a is any scoring expert, b is the total number of experts, pf at is the quality score of the image of the tth region given by the a-th scoring expert, x a is the weight coefficient of the ath scoring expert; Then, the agricultural land image quality qualification index s is compared with the preset quality qualification index threshold s x Make a comparison; If x ,The system judged that the quality of the agricultural land image was unqualified, the surveying and mapping results were inaccurate, and rescheduled the shooting;​ If s≥s x ,The system judges that the quality of the agricultural land image is qualified, the ,surveying results are accurate, and analyzes and evaluates the status of the ,agricultural land through the recognition and analysis module.

6. The agricultural land status detailed mapping system based on the Internet of Things according to claim 5 is characterized in that: The analysis process of the identification and analysis module includes: First, the required features in the images of agricultural land taken by drones are identified and recorded; And through the formula Calculate the pest impact coefficient in agricultural land; Then through the formula Calculate the growth coefficient δ of crops in the agricultural land; Among them, cb t is the number of worm spots in the t-th region image, cb y is the preset number of insect spots, cm t is the area of ​​the worm spot in the t-th region image, cb y is the preset insect spot area, ysld t is the color brightness of crops in the t-th region image, ysld y is the preset color brightness, pz t is the smoothness of the crop in the t-th region image, pz y is the preset smoothness, is the plant height change curve of crops in the t-th regional image, which is established by combining historical monitoring data, β t (x) is the leaf size change curve of the crop in the t-th region image, which is established by combining historical monitoring data. r1 and r2 are the starting time point in historical monitoring and the time point of the current detection, respectively.

7. The agricultural land status detailed mapping system based on the Internet of Things according to claim 6 is characterized in that: The analysis process of the identification and analysis module also includes: By comparing the growth coefficient δ of crops in the cultivated land with the preset growth coefficient threshold δ x Make a comparison; If δ<δ x , the system judges that the growth of crops in the farmland is slow, which reflects the lack of nutrition in the current farmland, and it is necessary to apply fertilizer in time to ensure that the land is fully nutritious; If δ ≥ δ x ,The system determines that the crops in the agricultural land grow fast, which reflects that the nutrition in the current agricultural land is sufficient and there is no need to apply fertilizer to ensure that the land is fully nutritious.

8. A method for mapping the state details of cultivated land based on the Internet of Things, the method adopts the system for mapping the state details of cultivated land based on the Internet of Things as claimed in claims 1-7, characterized in that: The method comprises: S1: Collect shooting environment data at different time points through the environment assessment module, and evaluate whether there is a good shooting environment at the current time point based on the environment data information; S2: When it is determined that there is a good shooting environment at the current time point, the image of the farmland is photographed and mapped by a drone; S3: Evaluate the agricultural land images taken by the drone through the surveying and mapping evaluation module, and determine whether the agricultural land images taken by the drone are qualified according to the evaluation results; S4: When the image of the cultivated land taken by the UAV is judged to be qualified, the recognition and analysis module is used to identify the features in the image, and the state of the cultivated land is analyzed and evaluated in combination with the data.

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