A botanical pesticide evaluation method and system based on data analysis

Abstract

The present invention discloses a botanical pesticide evaluation method and system based on data analysis. By acquiring the image data of vegetable worms in the vegetable field area and performing comprehensive image analysis, the larval growth data is obtained. According to the larval growth data, combined with the environmental data of the vegetable field area, the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index are calculated and analyzed. Through the evaluation index, the effect of the botanical pesticide can be analyzed more intuitively, and the evaluation accuracy of the botanical pesticide is improved. In addition, the present invention generates botanical pesticide adjustment plan information based on the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index. The plan information can further reasonably adjust the application plan of the botanical pesticide, thereby effectively assisting the grower in the use of the botanical pesticide, and then realizing the efficient use of the botanical pesticide.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and more specifically, to a botanical pesticide evaluation method and system based on data analysis. Background Art

[0002] Botanical pesticides are a branch of biological pesticides. They refer to plant-derived preparations that utilize the stable active ingredients contained in plants and use them on recipient plants in a certain way to protect them from or reduce the harm of harmful organisms such as diseases, insects, and weeds.

[0003] Various botanical pesticides are usually not a single compound, but all or part of the organic matter of plant organisms. The composition is complex and varied, but generally includes alkaloids, glycosides, toxic proteins, volatile essential oils, tannins, resins, organic acids, esters, ketones, terpenes and other substances.

[0004] However, due to the complex structure of botanical pesticides, difficulty in synthesis, slow efficacy and other reasons, it is difficult to make a reasonable evaluation of the efficacy of botanical pesticides and to apply them efficiently, which undoubtedly hinders the development of botanical pesticides. Therefore, there is an urgent need for an evaluation method for botanical pesticides. Summary of the invention

[0005] In order to solve at least one of the above technical problems, the present invention proposes a botanical pesticide evaluation based on data analysis.

[0006] The first aspect of the present invention provides a botanical pesticide evaluation method based on data analysis, comprising:

[0007] Acquire the image data of vegetable insects in the vegetable field area within a preset time;

[0008] Image preprocessing and characteristics analysis of cabbage worms are performed based on cabbage worm image data to obtain larvae growth data;

[0009] Obtain the environmental data of the vegetable field area and the concentration data of botanical pesticides, and calculate and analyze the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index based on the environmental data combined with the larval growth data;

[0010] Numerical analysis is performed based on the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index, and information on the botanical pesticide adjustment plan is generated.

[0011] In this solution, the method of obtaining the image data of vegetable insects in the vegetable field area within a preset time includes:

[0012] Construct a database of cabbage worm characteristics;

[0013] Obtain reference image data of cabbage worms from big data;

[0014] According to the reference image data of cabbage worms, feature recognition and feature extraction of larvae and adults are performed to obtain reference feature data of larvae and reference feature data of adults;

[0015] The larval reference characteristic data and the adult reference characteristic data are imported into the cabbage worm characteristic database.

[0016] In this scheme, the image preprocessing and the characteristics analysis of the vegetable worm are performed according to the vegetable worm image data to obtain the larval growth data, specifically:

[0017] Obtain cabbage worm image data;

[0018] The image data of the vegetable worm is smoothed and denoised to obtain enhanced image data of the vegetable worm;

[0019] Acquire larvae reference characteristic data and adult reference characteristic data from the cabbage worm characteristic database;

[0020] According to the enhanced cabbage worm image data, combined with the larval reference feature data and the adult reference feature data, the cabbage worm individual size analysis and image feature distribution calculation were performed to obtain the larval feature distribution data and the adult feature distribution data.

[0021] In this scheme, the image preprocessing and the characteristics analysis of the vegetable worm are performed according to the vegetable worm image data to obtain the larval growth data, specifically:

[0022] Quantitative analysis was performed based on the larval characteristic distribution data to obtain the larval density information per unit area;

[0023] Quantitative analysis was performed based on the distribution data of adult characteristics to obtain information on adult density per unit area;

[0024] According to the larval density information per unit area and the adult density information per unit area, combined with the size of the vegetable field area, the overall growth rate information of the larvae was obtained;

[0025] The larvae density information per unit area and the adult density information per unit area are analyzed in a comprehensive ratio to obtain the larvae occupancy ratio information;

[0026] The larval growth data were obtained by integrating the larval density information per unit area, the growth rate information of the total number of larvae, and the larval occupancy ratio information.

[0027] In this scheme, the method of obtaining the environmental data of the vegetable field area and the concentration data of the botanical pesticide, and calculating and analyzing the environmental data in combination with the larval growth data to obtain the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index includes:

[0028] Obtain overall image data of the vegetable field area;

[0029] Perform color feature analysis based on the overall image data to obtain a color feature distribution map;

[0030] Data analysis is performed based on the color feature distribution map to obtain a distribution map of planted vegetable leaves density;

[0031] According to the vegetable leaf planting density distribution map, the density is evenly divided to obtain multiple vegetable field sub-areas;

[0032] Sample vegetable leaves from multiple vegetable field sub-areas are obtained for phytosanitary testing to obtain phytosanitary concentration data.

[0033] In this scheme, the environmental data of the vegetable field area and the concentration data of the botanical pesticide are obtained, and the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index are obtained by calculation and analysis based on the environmental data combined with the larval growth data, specifically:

[0034] Numerical analysis and calculation were performed based on the larval growth data to obtain the larval inhibition evaluation index;

[0035] Based on the larval inhibition evaluation index, combined with the vegetable field regional environmental data and the botanical pesticide concentration data, a comprehensive calculation and analysis was performed to obtain the botanical pesticide effectiveness evaluation index.

[0036] In this scheme, the numerical analysis is performed based on the larvae inhibition evaluation index and the botanical pesticide effectiveness evaluation index to generate botanical pesticide adjustment scheme information, specifically:

[0037] Determine the larval inhibition evaluation index;

[0038] If the larval inhibition evaluation index is less than the first preset evaluation index, performing difference processing on the larval inhibition evaluation index and the first preset evaluation index, and obtaining the botanical pesticide concentration correction information according to the difference;

[0039] Determine the effectiveness evaluation index of botanical pesticides;

[0040] If the botanical pesticide effectiveness evaluation index is less than the second preset evaluation index, the botanical pesticide effectiveness evaluation index and the second preset evaluation index are subjected to difference processing, and the botanical pesticide spraying frequency correction information is obtained according to the difference;

[0041] The botanical pesticide concentration correction information and the botanical pesticide spraying frequency correction information are combined to obtain a botanical pesticide adjustment plan.

[0042] The second aspect of the present invention further provides a botanical pesticide evaluation system based on data analysis, the system comprising: a memory, a processor, the memory comprising a botanical pesticide evaluation method program based on data analysis, the botanical pesticide evaluation method program based on data analysis being executed by the processor to implement the following steps:

[0043] Acquire the image data of vegetable insects in the vegetable field area within a preset time;

[0044] Image preprocessing and characteristics analysis of cabbage worms are performed based on cabbage worm image data to obtain larvae growth data;

[0045] Obtain the environmental data of the vegetable field area and the concentration data of botanical pesticides, and calculate and analyze the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index based on the environmental data combined with the larval growth data;

[0046] Numerical analysis is performed based on the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index, and information on the botanical pesticide adjustment plan is generated.

[0047] In this scheme, the method of obtaining the environmental data of the vegetable field area and the concentration data of the botanical pesticide, and calculating and analyzing the environmental data in combination with the larval growth data to obtain the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index includes:

[0048] Obtain overall image data of the vegetable field area;

[0049] Perform color feature analysis based on the overall image data to obtain a color feature distribution map;

[0050] Data analysis is performed based on the color feature distribution map to obtain a distribution map of planted vegetable leaves density;

[0051] According to the vegetable leaf planting density distribution map, the density is evenly divided to obtain multiple vegetable field sub-areas;

[0052] Sample vegetable leaves from multiple vegetable field sub-areas are obtained for phytosanitary testing to obtain phytosanitary concentration data.

[0053] In this scheme, the numerical analysis is performed based on the larvae inhibition evaluation index and the botanical pesticide effectiveness evaluation index to generate botanical pesticide adjustment scheme information, specifically:

[0054] Determine the larval inhibition evaluation index;

[0055] If the larval inhibition evaluation index is less than the first preset evaluation index, performing difference processing on the larval inhibition evaluation index and the first preset evaluation index, and obtaining the botanical pesticide concentration correction information according to the difference;

[0056] Determine the effectiveness evaluation index of botanical pesticides;

[0057] If the botanical pesticide effectiveness evaluation index is less than the second preset evaluation index, the botanical pesticide effectiveness evaluation index and the second preset evaluation index are subjected to difference processing, and the botanical pesticide spraying frequency correction information is obtained according to the difference;

[0058] The botanical pesticide concentration correction information and the botanical pesticide spraying frequency correction information are combined to obtain a botanical pesticide adjustment plan.

[0059] The present invention discloses a botanical pesticide evaluation method and system based on data analysis. By acquiring the image data of vegetable worms in the vegetable field area and performing comprehensive image analysis, the larval growth data is obtained. According to the larval growth data, combined with the environmental data of the vegetable field area, the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index are calculated and analyzed. Through the evaluation index, the effect of the botanical pesticide can be analyzed more intuitively, and the evaluation accuracy of the botanical pesticide is improved. In addition, the present invention generates botanical pesticide adjustment plan information based on the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index. The plan information can further reasonably adjust the application plan of the botanical pesticide, thereby effectively assisting the grower in the use of the botanical pesticide, and then realizing the efficient use of the botanical pesticide. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A flow chart of a botanical pesticide evaluation method based on data analysis of the present invention is shown;

[0061] Figure 2 The flow chart of obtaining larval growth data of the present invention is shown;

[0062] Figure 3 The flow chart of obtaining botanical pesticide concentration data of the present invention is shown;

[0063] Figure 4 A block diagram of a botanical pesticide evaluation system based on data analysis of the present invention is shown. DETAILED DESCRIPTION

[0064] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0066] Figure 1 A flow chart of a botanical pesticide evaluation method based on data analysis of the present invention is shown.

[0067] like Figure 1 As shown, the first aspect of the present invention provides a botanical pesticide evaluation method based on data analysis, comprising:

[0068] S102, obtaining image data of vegetable insects in a vegetable field area within a preset time;

[0069] S104, performing image preprocessing and cabbage worm feature analysis according to the cabbage worm image data to obtain larvae growth data;

[0070] S106, obtaining the environmental data of the vegetable field area and the concentration data of the botanical pesticide, and calculating and analyzing the environmental data in combination with the larvae growth data to obtain the larvae inhibition evaluation index and the botanical pesticide effectiveness evaluation index;

[0071] S108, performing numerical analysis based on the larvae inhibition evaluation index and the botanical pesticide effectiveness evaluation index and generating botanical pesticide adjustment plan information.

[0072] It should be noted that the preset time is generally a growth cycle of vegetable worms in the vegetable field area, and is also the spraying cycle of botanical pesticides. The botanical pesticides are generally larvae-inhibiting botanical pesticides.

[0073] Within a preset time, the image data of vegetable insects in the vegetable field area is obtained, which includes:

[0074] Construct a database of cabbage insect characteristics;

[0075] Obtain reference image data of cabbage worms from big data;

[0076] According to the reference image data of cabbage worms, feature recognition and feature extraction of larvae and adults are performed to obtain reference feature data of larvae and reference feature data of adults;

[0077] The larvae reference feature data and the adult reference feature data are imported into the vegetable worm feature database. It should be noted that the vegetable worm image data is obtained by a video image camera device.

[0078] Image preprocessing and cabbage worm feature analysis were performed based on the cabbage worm image data to obtain larval growth data, specifically:

[0079] Obtain cabbage worm image data;

[0080] The image data of the vegetable worm is smoothed and denoised to obtain enhanced image data of the vegetable worm;

[0081] Acquire larvae reference characteristic data and adult reference characteristic data from the cabbage worm characteristic database;

[0082] According to the enhanced cabbage worm image data, combined with the larval reference feature data and the adult reference feature data, the cabbage worm individual size analysis and image feature distribution calculation were performed to obtain the larval feature distribution data and the adult feature distribution data.

[0083] It should be noted that, in the obtained larval characteristic distribution data and adult characteristic distribution data, the distribution data is specifically the density distribution data of cabbage loopers in the image.

[0084] Figure 2 The flowchart of obtaining larval growth data of the present invention is shown.

[0085] According to an embodiment of the present invention, the image preprocessing and the characteristic analysis of the vegetable worm are performed based on the vegetable worm image data to obtain the larval growth data, specifically:

[0086] S202, performing quantitative analysis based on the larvae characteristic distribution data to obtain larvae density information per unit area;

[0087] S204, performing quantitative analysis based on the adult characteristic distribution data to obtain adult density information per unit area;

[0088] S206, obtaining information on the overall growth rate of the number of larvae according to the larvae density information per unit area and the adult density information per unit area, combined with the size of the vegetable field area;

[0089] S208, performing a comprehensive ratio analysis on the larva density information per unit area and the adult density information per unit area to obtain larvae occupancy ratio information;

[0090] S210, integrating the larva density information of the unit area, the growth rate information of the total number of larvae, and the larvae occupancy ratio information to obtain larvae growth data.

[0091] It should be noted that the information on the overall growth rate of larvae is obtained based on the larval density information per unit area and the adult density information per unit area, combined with the size of the vegetable field area. As the larvae grow, some of them will become adults. Therefore, in obtaining the information on the overall growth rate of larvae, it is necessary to conduct a comprehensive analysis in combination with the adult density information per unit area.

[0092] Figure 3 The flowchart of obtaining botanical pesticide concentration data of the present invention is shown.

[0093] According to an embodiment of the present invention, the method of obtaining the environmental data of the vegetable field area and the botanical pesticide concentration data, and calculating and analyzing the environmental data in combination with the larval growth data to obtain the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index, comprises:

[0094] S302, obtaining overall image data of the vegetable field area;

[0095] S304, performing color feature analysis based on the overall image data to obtain a color feature distribution map;

[0096] S306, performing data analysis according to the color feature distribution map to obtain a planting leaf density distribution map;

[0097] S308, performing density average division according to the vegetable leaf planting density distribution map to obtain a plurality of vegetable plot sub-areas;

[0098] S310, obtaining sample vegetable leaves from multiple vegetable field sub-areas for testing of botanical pesticides to obtain botanical pesticide concentration data.

[0099] It should be noted that the overall image data of the vegetable field area is obtained by a video image camera. The botanical pesticide concentration data includes the early concentration information, late concentration information and concentration change rate of the botanical pesticide. In addition, since the color of the vegetable leaves is quite different from the color of the soil in the vegetable field area, an accurate density distribution map of planted vegetable leaves can be obtained by analyzing the color characteristics. According to the density distribution, regional averaging and sub-regional concentration detection are performed to obtain more accurate botanical pesticide concentration data, thereby improving the accuracy of subsequent evaluation of botanical pesticides.

[0100] According to an embodiment of the present invention, the vegetable field area environmental data and botanical pesticide concentration data are obtained, and the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index are obtained by calculation and analysis based on the environmental data combined with the larval growth data, specifically:

[0101] Numerical analysis and calculation were performed based on the larval growth data to obtain the larval inhibition evaluation index;

[0102] Based on the larval inhibition evaluation index, combined with the vegetable field regional environmental data and the botanical pesticide concentration data, a comprehensive calculation and analysis was performed to obtain the botanical pesticide effectiveness evaluation index.

[0103] It should be noted that the larval inhibition evaluation index is an important indicator reflecting the inhibitory effect of botanical pesticides on larvae, and has important reference significance for the evaluation of the effect of botanical pesticides. The vegetable field area environmental data includes the light time, soil pH, temperature, etc. of the vegetable field area. The botanical pesticide effectiveness evaluation index is a comprehensive evaluation index reflecting the effect of botanical pesticides.

[0104] In addition, the specific calculation formula of the larvae inhibition evaluation index is:

[0105]

[0106] Among them, E B is the larval inhibition evaluation index, M is the total number of larvae per unit area, ρ i is the larvae density value of the i-th unit area, R c is the overall growth rate of larvae, N c is the larvae occupancy ratio;

[0107] The specific calculation formula of the botanical pesticide effectiveness evaluation index is:

[0108] E X =log(E B +F) 2 ×ln T s

[0109] Among them, F is the change rate of botanical pesticide concentration, T s is the average illumination time.

[0110] According to an embodiment of the present invention, the numerical analysis is performed based on the larvae inhibition evaluation index and the botanical pesticide effectiveness evaluation index to generate botanical pesticide adjustment plan information, specifically:

[0111] Determine the larval inhibition evaluation index;

[0112] If the larval inhibition evaluation index is less than the first preset evaluation index, performing difference processing on the larval inhibition evaluation index and the first preset evaluation index, and obtaining the botanical pesticide concentration correction information according to the difference;

[0113] Determine the effectiveness evaluation index of botanical pesticides;

[0114] If the botanical pesticide effectiveness evaluation index is less than the second preset evaluation index, the botanical pesticide effectiveness evaluation index and the second preset evaluation index are subjected to difference processing, and the botanical pesticide spraying frequency correction information is obtained according to the difference;

[0115] The botanical pesticide concentration correction information and the botanical pesticide spraying frequency correction information are combined to obtain a botanical pesticide adjustment plan.

[0116] It should be noted that if the larval inhibition evaluation index is less than the first preset evaluation index, it means that the inhibition effect of the botanical pesticide is low, and the pesticide concentration needs to be corrected at this time. If the botanical pesticide effectiveness evaluation index is less than the second preset evaluation index, it means that the comprehensive control effect of the botanical pesticide is low, and the pesticide frequency needs to be corrected. Reasonable adjustment of the application of botanical pesticides through botanical pesticide adjustment plan information can effectively assist growers in the use of botanical pesticides.

[0117] According to an embodiment of the present invention, it also includes:

[0118] Select test botanical pesticides for spraying;

[0119] According to the preset time period, obtain the video data of the vegetable field area;

[0120] Perform image frame splitting processing according to the video data of the vegetable field area to obtain image set data;

[0121] Performing vegetable worm feature recognition and vegetable worm feature displacement analysis on the image set data to obtain vegetable worm distribution data and vegetable worm displacement data based on image frame analysis;

[0122] Based on the comprehensive analysis of cabbage worm distribution data and cabbage worm displacement data, the information on the change of cabbage worm activity frequency was obtained;

[0123] The success rate of cabbage worms avoiding feeding was obtained by comprehensive analysis based on the information on changes in the activity frequency of cabbage worms.

[0124] It should be noted that the preset time period is a cycle time for the repellent plant pesticide to be sprayed, which is generally 1 to 3 months. The test plant pesticide is generally a repellent plant pesticide. By analyzing the change information of the activity frequency of the cabbage worm, the success rate of the cabbage worm avoidance can be further obtained, so that the repellent and avoidance effect of the repellent plant pesticide on the cabbage worm can be effectively evaluated, so that growers can more intuitively compare the effects of the plant pesticide.

[0125] The cabbage worm distribution data specifically includes the number of cabbage worms distributed in the air and the amount of cabbage worms distributed on the leaves. The cabbage worm displacement data includes the overall average displacement of cabbage worms entering and leaving the vegetable field area within a preset time period, and the cabbage worm displacement data can effectively reflect the activity trend of cabbage worms in the vegetable field area.

[0126] According to an embodiment of the present invention, it also includes:

[0127] Acquire larvae growth data in real time based on cabbage worm image data;

[0128] According to the larval growth data, the larval density information per unit area and the adult density information per unit area are obtained;

[0129] In a preset time period, current larval occupancy ratio information and adult occupancy ratio information are calculated through the larval density information of the unit area and the adult density information of the unit area;

[0130] Compare the current larvae occupancy ratio information and adult occupancy ratio information with the larvae occupancy ratio information and adult occupancy ratio information in the previous cycle to obtain a larvae ratio change value and an adult ratio change value;

[0131] Obtain information on the action categories of various botanical pesticides;

[0132] According to the action category information of various botanical pesticides, combined with the change values ​​of larval ratio and adult ratio, effect analysis is carried out to obtain the correction information of botanical pesticide concentration.

[0133] It should be noted that in some planting areas, it is necessary to use a variety of botanical pesticides for the comprehensive prevention and control of vegetable worms, and the prevention and control effects of different pesticides on larvae and adults are different. By analyzing the change values ​​of the larval ratio and the adult ratio, it is possible to comprehensively analyze the correction information of different types of botanical pesticides, thereby improving the effective utilization rate of botanical pesticides and the efficiency of preventing and controlling vegetable worms. The botanical pesticide concentration correction information includes the application concentration correction information and application amount correction information of different types of botanical pesticides. The action category information of the multiple botanical pesticides is specifically for the larval action category and the adult action category. The preset time period includes multiple botanical pesticide spraying cycles, and the cycle is generally 5 to 20 days.

[0134] Figure 4 A block diagram of a botanical pesticide evaluation system based on data analysis of the present invention is shown.

[0135] The second aspect of the present invention further provides a botanical pesticide evaluation system 4 based on data analysis, the system comprising: a memory 41, a processor 42, the memory comprising a botanical pesticide evaluation method program based on data analysis, the botanical pesticide evaluation method program based on data analysis being executed by the processor to implement the following steps:

[0136] Acquire the image data of vegetable insects in the vegetable field area within a preset time;

[0137] Image preprocessing and characteristics analysis of cabbage worms are performed based on cabbage worm image data to obtain larvae growth data;

[0138] Obtain the environmental data of the vegetable field area and the concentration data of botanical pesticides, and calculate and analyze the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index based on the environmental data combined with the larval growth data;

[0139] Numerical analysis is performed based on the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index, and information on the botanical pesticide adjustment plan is generated.

[0140] It should be noted that the preset time is generally a growth cycle of vegetable worms in the vegetable field area, and is also the spraying cycle of botanical pesticides. The botanical pesticides are generally larvae-inhibiting botanical pesticides.

[0141] Within a preset time, the image data of vegetable insects in the vegetable field area is obtained, which includes:

[0142] Construct a database of cabbage insect characteristics;

[0143] Obtain reference image data of cabbage worms from big data;

[0144] According to the reference image data of cabbage worms, feature recognition and feature extraction of larvae and adults are performed to obtain reference feature data of larvae and reference feature data of adults;

[0145] The larvae reference feature data and the adult reference feature data are imported into the vegetable worm feature database. It should be noted that the vegetable worm image data is obtained by a video image camera device.

[0146] Image preprocessing and cabbage worm feature analysis were performed based on the cabbage worm image data to obtain larval growth data, specifically:

[0147] Obtain cabbage worm image data;

[0148] The image data of the vegetable worm is smoothed and denoised to obtain enhanced image data of the vegetable worm;

[0149] Acquire larvae reference characteristic data and adult reference characteristic data from the cabbage worm characteristic database;

[0150] According to the enhanced cabbage worm image data, combined with the larval reference feature data and the adult reference feature data, the cabbage worm individual size analysis and image feature distribution calculation were performed to obtain the larval feature distribution data and the adult feature distribution data.

[0151] It should be noted that, in the obtained larval characteristic distribution data and adult characteristic distribution data, the distribution data is specifically the density distribution data of cabbage loopers in the image.

[0152] According to an embodiment of the present invention, the image preprocessing and the characteristic analysis of the vegetable worm are performed based on the vegetable worm image data to obtain the larval growth data, specifically:

[0153] Quantitative analysis was performed based on the larval characteristic distribution data to obtain the larval density information per unit area;

[0154] Quantitative analysis was performed based on the distribution data of adult characteristics to obtain information on adult density per unit area;

[0155] According to the larval density information per unit area and the adult density information per unit area, combined with the size of the vegetable field area, the overall growth rate information of the larvae was obtained;

[0156] The larvae density information per unit area and the adult density information per unit area are analyzed in a comprehensive ratio to obtain the larvae occupancy ratio information;

[0157] The larval growth data were obtained by integrating the larval density information per unit area, the growth rate information of the total number of larvae, and the larval occupancy ratio information.

[0158] It should be noted that the information on the overall growth rate of larvae is obtained based on the larval density information per unit area and the adult density information per unit area, combined with the size of the vegetable field area. As the larvae grow, some of them will become adults. Therefore, in obtaining the information on the overall growth rate of larvae, it is necessary to conduct a comprehensive analysis in combination with the adult density information per unit area.

[0159] According to an embodiment of the present invention, the method of obtaining the environmental data of the vegetable field area and the botanical pesticide concentration data, and calculating and analyzing the environmental data in combination with the larval growth data to obtain the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index, comprises:

[0160] Obtain overall image data of the vegetable field area;

[0161] Perform color feature analysis based on the overall image data to obtain a color feature distribution map;

[0162] Data analysis is performed based on the color feature distribution map to obtain a distribution map of planted vegetable leaves density;

[0163] According to the vegetable leaf planting density distribution map, the density is evenly divided to obtain multiple vegetable field sub-areas;

[0164] Sample vegetable leaves from multiple vegetable field sub-areas are obtained for phytosanitary testing to obtain phytosanitary concentration data.

[0165] It should be noted that the overall image data of the vegetable field area is obtained by a video image camera. The botanical pesticide concentration data includes the early concentration information, late concentration information and concentration change rate of the botanical pesticide. In addition, since the color of the vegetable leaves is quite different from the color of the soil in the vegetable field area, an accurate density distribution map of planted vegetable leaves can be obtained by analyzing the color characteristics. According to the density distribution, regional averaging and sub-regional concentration detection are performed to obtain more accurate botanical pesticide concentration data, thereby improving the accuracy of subsequent evaluation of botanical pesticides.

[0166] According to an embodiment of the present invention, the vegetable field area environmental data and botanical pesticide concentration data are obtained, and the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index are obtained by calculation and analysis based on the environmental data combined with the larval growth data, specifically:

[0167] Numerical analysis and calculation were performed based on the larval growth data to obtain the larval inhibition evaluation index;

[0168] Based on the larval inhibition evaluation index, combined with the vegetable field regional environmental data and the botanical pesticide concentration data, a comprehensive calculation and analysis was performed to obtain the botanical pesticide effectiveness evaluation index.

[0169] It should be noted that the larval inhibition evaluation index is an important indicator reflecting the inhibitory effect of botanical pesticides on larvae, and has important reference significance for the evaluation of the effect of botanical pesticides. The vegetable field area environmental data includes the light time, soil pH, temperature, etc. of the vegetable field area. The botanical pesticide effectiveness evaluation index is a comprehensive evaluation index reflecting the effect of botanical pesticides.

[0170] In addition, the specific calculation formula of the larvae inhibition evaluation index is:

[0171]

[0172] Among them, E B is the larval inhibition evaluation index, M is the total number of larvae per unit area, ρ i is the larvae density value of the i-th unit area, R c is the overall growth rate of larvae, N c is the larvae occupancy ratio;

[0173] The specific calculation formula of the botanical pesticide effectiveness evaluation index is:

[0174] E X =log(E B +F) 2 ×ln T s

[0175] Among them, F is the change rate of botanical pesticide concentration, T s is the average illumination time.

[0176] According to an embodiment of the present invention, the numerical analysis is performed based on the larvae inhibition evaluation index and the botanical pesticide effectiveness evaluation index to generate botanical pesticide adjustment plan information, specifically:

[0177] Determine the larval inhibition evaluation index;

[0178] If the larval inhibition evaluation index is less than the first preset evaluation index, performing difference processing on the larval inhibition evaluation index and the first preset evaluation index, and obtaining the botanical pesticide concentration correction information according to the difference;

[0179] Determine the effectiveness evaluation index of botanical pesticides;

[0180] If the botanical pesticide effectiveness evaluation index is less than the second preset evaluation index, the botanical pesticide effectiveness evaluation index and the second preset evaluation index are subjected to difference processing, and the botanical pesticide spraying frequency correction information is obtained according to the difference;

[0181] The botanical pesticide concentration correction information and the botanical pesticide spraying frequency correction information are combined to obtain a botanical pesticide adjustment plan.

[0182] It should be noted that if the larval inhibition evaluation index is less than the first preset evaluation index, it means that the inhibition effect of the botanical pesticide is low, and the pesticide concentration needs to be corrected at this time. If the botanical pesticide effectiveness evaluation index is less than the second preset evaluation index, it means that the comprehensive control effect of the botanical pesticide is low, and the pesticide frequency needs to be corrected. Reasonable adjustment of the application of botanical pesticides through botanical pesticide adjustment plan information can effectively assist growers in the use of botanical pesticides.

[0183] According to an embodiment of the present invention, it also includes:

[0184] Select test botanical pesticides for spraying;

[0185] According to the preset time period, obtain the video data of the vegetable field area;

[0186] Perform image frame splitting processing according to the video data of the vegetable field area to obtain image set data;

[0187] Performing vegetable worm feature recognition and vegetable worm feature displacement analysis on the image set data to obtain vegetable worm distribution data and vegetable worm displacement data based on image frame analysis;

[0188] Based on the comprehensive analysis of cabbage worm distribution data and cabbage worm displacement data, the information on the change of cabbage worm activity frequency was obtained;

[0189] The success rate of cabbage worms avoiding feeding was obtained by comprehensive analysis based on the information on changes in the activity frequency of cabbage worms.

[0190] It should be noted that the preset time period is a cycle time for the repellent plant pesticide to be sprayed, which is generally 1 to 3 months. The test plant pesticide is generally a repellent plant pesticide. By analyzing the change information of the activity frequency of the cabbage worm, the success rate of the cabbage worm avoidance can be further obtained, so that the repellent and avoidance effect of the repellent plant pesticide on the cabbage worm can be effectively evaluated, so that growers can more intuitively compare the effects of the plant pesticide.

[0191] The cabbage worm distribution data specifically includes the number of cabbage worms distributed in the air and the amount of cabbage worms distributed on the leaves. The cabbage worm displacement data includes the overall average displacement of cabbage worms entering and leaving the vegetable field area within a preset time period, and the cabbage worm displacement data can effectively reflect the activity trend of cabbage worms in the vegetable field area.

[0192] According to an embodiment of the present invention, it also includes:

[0193] Acquire larvae growth data in real time based on cabbage worm image data;

[0194] According to the larval growth data, the larval density information per unit area and the adult density information per unit area are obtained;

[0195] In a preset time period, current larval occupancy ratio information and adult occupancy ratio information are calculated through the larval density information of the unit area and the adult density information of the unit area;

[0196] Compare the current larvae occupancy ratio information and adult occupancy ratio information with the larvae occupancy ratio information and adult occupancy ratio information in the previous cycle to obtain a larvae ratio change value and an adult ratio change value;

[0197] Obtain information on the action categories of various botanical pesticides;

[0198] According to the action category information of various botanical pesticides, combined with the change values ​​of larval ratio and adult ratio, effect analysis is carried out to obtain the correction information of botanical pesticide concentration.

[0199] It should be noted that in some planting areas, it is necessary to use a variety of botanical pesticides for the comprehensive prevention and control of vegetable worms, and the prevention and control effects of different pesticides on larvae and adults are different. By analyzing the change values ​​of the larval ratio and the adult ratio, it is possible to comprehensively analyze the correction information of different types of botanical pesticides, thereby improving the effective utilization rate of botanical pesticides and the efficiency of preventing and controlling vegetable worms. The botanical pesticide concentration correction information includes the application concentration correction information and application amount correction information of different types of botanical pesticides. The action category information of the multiple botanical pesticides is specifically for the larval action category and the adult action category. The preset time period includes multiple botanical pesticide spraying cycles, and the cycle is generally 5 to 20 days.

[0200] The present invention discloses a botanical pesticide evaluation method and system based on data analysis. By acquiring the image data of vegetable worms in the vegetable field area and performing comprehensive image analysis, the larval growth data is obtained. According to the larval growth data, combined with the environmental data of the vegetable field area, the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index are calculated and analyzed. Through the evaluation index, the effect of the botanical pesticide can be analyzed more intuitively, and the evaluation accuracy of the botanical pesticide is improved. In addition, the present invention generates botanical pesticide adjustment plan information based on the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index. The plan information can further reasonably adjust the application plan of the botanical pesticide, thereby effectively assisting the grower in the use of the botanical pesticide, and then realizing the efficient use of the botanical pesticide.

[0201] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0202] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0203] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0204] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0205] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0206] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A botanical pesticide evaluation method based on data analysis, characterized in that: include: Acquire the image data of vegetable insects in the vegetable field area within a preset time; Image preprocessing and characteristics analysis of cabbage worms are performed based on cabbage worm image data to obtain larvae growth data; Obtain the environmental data of the vegetable field area and the concentration data of botanical pesticides, and calculate and analyze the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index based on the environmental data combined with the larval growth data; Conduct numerical analysis based on the larval suppression evaluation index and the botanical pesticide effectiveness evaluation index and generate botanical pesticide adjustment plan information; The image preprocessing and the characteristic analysis of the vegetable worms are performed according to the vegetable worm image data to obtain the larvae growth data, specifically: Obtain cabbage worm image data; The image data of the vegetable worm is smoothed and denoised to obtain enhanced image data of the vegetable worm; Acquire larvae reference characteristic data and adult reference characteristic data from the cabbage worm characteristic database; According to the enhanced cabbage worm image data, the cabbage worm individual size analysis and image feature distribution calculation are performed in combination with the larvae reference feature data and the adult reference feature data to obtain the larvae feature distribution data and the adult feature distribution data; The image preprocessing and the characteristic analysis of the vegetable worms are performed according to the vegetable worm image data to obtain the larvae growth data, specifically: Quantitative analysis was performed based on the larval characteristic distribution data to obtain the larval density information per unit area; Quantitative analysis was performed based on the distribution data of adult characteristics to obtain information on adult density per unit area; According to the larval density information per unit area and the adult density information per unit area, combined with the size of the vegetable field area, the overall growth rate information of the larvae was obtained; The larvae density information per unit area and the adult density information per unit area are analyzed in a comprehensive ratio to obtain the larvae occupancy ratio information; The larval growth data are obtained by integrating the larval density information of the unit area, the growth rate information of the total number of larvae, and the larval occupancy ratio information; The method of obtaining the environmental data of the vegetable field area and the concentration data of the botanical pesticide, and calculating and analyzing the environmental data in combination with the larval growth data to obtain the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index, includes: Obtain overall image data of the vegetable field area; Perform color feature analysis based on the overall image data to obtain a color feature distribution map; Data analysis is performed based on the color feature distribution map to obtain a distribution map of planted vegetable leaves density; According to the vegetable leaf planting density distribution map, the density is evenly divided to obtain multiple vegetable field sub-areas; Obtaining sample vegetable leaves from multiple vegetable plots for testing of botanical pesticides to obtain botanical pesticide concentration data; The method of obtaining the environmental data of the vegetable field area and the concentration data of the botanical pesticide, and calculating and analyzing the environmental data in combination with the larval growth data to obtain the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index is as follows: Numerical analysis and calculation were performed based on the larval growth data to obtain the larval inhibition evaluation index; Based on the larval inhibition evaluation index, the vegetable field regional environmental data and the botanical pesticide concentration data were combined for comprehensive calculation and analysis to obtain the botanical pesticide effectiveness evaluation index; The specific calculation formula of the larvae inhibition evaluation index is: ， in, is the larvae inhibition evaluation index, M is the total number of larvae per unit area, is the larvae density value of the i-th unit area, is the overall larval population growth rate, is the larvae occupancy ratio; The specific calculation formula of the botanical pesticide effectiveness evaluation index is: ， Where, F is the rate of change of botanical pesticide concentration, is the average illumination time.

2. A botanical pesticide evaluation method based on data analysis according to claim 1, characterized in that: The step of obtaining the image data of vegetable insects in the vegetable field area within a preset time includes: Construct a database of cabbage worm characteristics; Obtain reference image data of cabbage worms from big data; According to the reference image data of cabbage worms, feature recognition and feature extraction of larvae and adults are performed to obtain reference feature data of larvae and reference feature data of adults; The larval reference characteristic data and the adult reference characteristic data are imported into the cabbage worm characteristic database.

3. A botanical pesticide evaluation method based on data analysis according to claim 1, characterized in that: The numerical analysis is performed based on the larvae inhibition evaluation index and the botanical pesticide effectiveness evaluation index to generate botanical pesticide adjustment plan information, specifically: Determine the larval inhibition evaluation index; If the larval inhibition evaluation index is less than the first preset evaluation index, performing difference processing on the larval inhibition evaluation index and the first preset evaluation index, and obtaining the botanical pesticide concentration correction information according to the difference; Determine the effectiveness evaluation index of botanical pesticides; If the botanical pesticide effectiveness evaluation index is less than the second preset evaluation index, the botanical pesticide effectiveness evaluation index and the second preset evaluation index are subjected to difference processing, and the botanical pesticide spraying frequency correction information is obtained according to the difference; The botanical pesticide concentration correction information and the botanical pesticide spraying frequency correction information are combined to obtain a botanical pesticide adjustment plan.

4. A botanical pesticide evaluation system based on data analysis, characterized in that: The system includes: a memory and a processor, wherein the memory includes a botanical pesticide evaluation method program based on data analysis, and when the botanical pesticide evaluation method program based on data analysis is executed by the processor, the following steps are implemented: Acquire the image data of vegetable insects in the vegetable field area within a preset time; Image preprocessing and characteristics analysis of cabbage worms are performed based on cabbage worm image data to obtain larvae growth data; Obtain the environmental data of the vegetable field area and the concentration data of botanical pesticides, and calculate and analyze the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index based on the environmental data combined with the larval growth data; Conduct numerical analysis based on the larval suppression evaluation index and the botanical pesticide effectiveness evaluation index and generate botanical pesticide adjustment plan information; The image preprocessing and the characteristic analysis of the vegetable worms are performed according to the vegetable worm image data to obtain the larvae growth data, specifically: Obtain cabbage worm image data; The image data of the vegetable worm is smoothed and denoised to obtain enhanced image data of the vegetable worm; Acquire larvae reference characteristic data and adult reference characteristic data from the cabbage worm characteristic database; According to the enhanced cabbage worm image data, the cabbage worm individual size analysis and image feature distribution calculation are performed in combination with the larvae reference feature data and the adult reference feature data to obtain the larvae feature distribution data and the adult feature distribution data; The image preprocessing and the characteristic analysis of the vegetable worms are performed according to the vegetable worm image data to obtain the larvae growth data, specifically: Quantitative analysis was performed based on the larval characteristic distribution data to obtain the larval density information per unit area; Quantitative analysis was performed based on the distribution data of adult characteristics to obtain information on adult density per unit area; According to the larval density information per unit area and the adult density information per unit area, combined with the size of the vegetable field area, the overall growth rate information of the larvae was obtained; The larvae density information per unit area and the adult density information per unit area are analyzed in a comprehensive ratio to obtain the larvae occupancy ratio information; The larval growth data are obtained by integrating the larval density information of the unit area, the growth rate information of the total number of larvae, and the larval occupancy ratio information; The method of obtaining the environmental data of the vegetable field area and the concentration data of the botanical pesticide, and calculating and analyzing the environmental data in combination with the larval growth data to obtain the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index, includes: Obtain overall image data of the vegetable field area; Perform color feature analysis based on the overall image data to obtain a color feature distribution map; Data analysis is performed based on the color feature distribution map to obtain a distribution map of planted vegetable leaves density; According to the vegetable leaf planting density distribution map, the density is evenly divided to obtain multiple vegetable field sub-areas; Obtaining sample vegetable leaves from multiple vegetable plots for testing of botanical pesticides to obtain botanical pesticide concentration data; The method of obtaining the environmental data of the vegetable field area and the concentration data of the botanical pesticide, and calculating and analyzing the environmental data in combination with the larval growth data to obtain the larval inhibition evaluation index and the botanical pesticide effectiveness evaluation index is as follows: Numerical analysis and calculation were performed based on the larval growth data to obtain the larval inhibition evaluation index; Based on the larval inhibition evaluation index, the vegetable field regional environmental data and the botanical pesticide concentration data were combined for comprehensive calculation and analysis to obtain the botanical pesticide effectiveness evaluation index; The specific calculation formula of the larvae inhibition evaluation index is: ， in, is the larvae inhibition evaluation index, M is the total number of larvae per unit area, is the larvae density value of the i-th unit area, is the overall larval population growth rate, is the larvae occupancy ratio; The specific calculation formula of the botanical pesticide effectiveness evaluation index is: ， Where, F is the rate of change of botanical pesticide concentration, is the average illumination time.

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