Intelligent precision agriculture comprehensive control method and system

Abstract

The invention provides an intelligent precision agriculture comprehensive control method and system, and relates to the technical field of agricultural production, and the method comprises the steps: setting a plurality of image collection devices and first sensor combinations at a plurality of positions in a target farmland; arranging a plurality of second sensor combinations at a plurality of positions of the soil of the target farmland; acquiring a farmland insect pest image acquired by image acquisition equipment; determining a pest severity coefficient according to the farmland pest image; acquiring environment data; acquiring soil environment data; determining whether pesticides are used for controlling insect pests or not; if the pesticide needs to be used for controlling the insect pests, the pesticide dosage is determined, and the insect pests are controlled according to the pesticide dosage. According to the method and the device, whether the pesticide is used for controlling the insect pests or not can be judged according to the severity of the insect pests, the environment condition and the soil environment condition, the pesticide dosage is determined, and the effectiveness and the accuracy of agricultural insect pest control are improved.

Description

Technical Field

[0001] The present invention relates to the field of agricultural production technology, and in particular to an intelligent precision agriculture comprehensive control method and system. Background Art

[0002] In the related art, CN102612974A discloses a planting method of compound circular agriculture, comprising the following steps: planting evergreen first tall trees and / or small shrubs around the cash crop planting site, the first tall trees and / or small shrubs forming a wind and sand protection belt for the cash crop; planting second tall trees outside the wind and sand protection belt, the second tall trees forming a hedge belt for the cash crop; planting first pest-proof plants that can prevent insect pests between the wind and sand protection belt and the hedge belt, the first pest-proof plants forming a first pest and disease protection belt for the cash crop. This scheme plants a variety of trees, shrubs and grasses around the cash crop site to form a wind and sand protection belt, a first pest and disease protection belt and a hedge belt for the cash crop, respectively, to provide a suitable growth environment for the cash crop, to ensure that the cash crop is not damaged by wind and sand and pests, to reduce the use of pesticides, and to improve the quality of agricultural products.

[0003] CN112997833A discloses a method for planting Polygonatum cyrtonema against lodging and pests based on eucalyptus forests. The method for planting Polygonatum cyrtonema against lodging and pests based on eucalyptus forests includes the following steps: adding rice washing water and microbial additives to eucalyptus leaves and peanut bran for mixing to obtain a product to be fermented; adding the product to be fermented to the eucalyptus head for fermentation; planting Polygonatum cyrtonema at the eucalyptus head once, so that Polygonatum cyrtonema is planted around the eucalyptus head; planting shrubs at the eucalyptus head for a second time, so that the shrubs are planted around the Polygonatum cyrtonema. The method for planting Polygonatum cyrtonema against lodging and pests based on eucalyptus forests can improve the ability of Polygonatum cyrtonema against lodging and pests and the growth effect of Polygonatum cyrtonema.

[0004] Based on the above-mentioned related technologies, the use of pesticides can be reduced and the quality of agricultural products can be improved. However, the related technologies do not take into account the impact of the severity of pests, environmental conditions and soil conditions on the use of pesticides, that is, it is impossible to judge whether to use pesticides and control the amount of pesticides based on the severity of pests, environmental conditions and soil conditions.

[0005] The information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides an intelligent precision agriculture comprehensive control method and system, which can solve the technical problem that related technologies cannot judge whether to use pesticides according to the severity of insect pests in farmland, environmental conditions and soil conditions, and control the amount of pesticides used.

[0007] According to a first aspect of the present invention, there is provided an intelligent precision agriculture integrated control method, comprising: A plurality of image acquisition devices and a first sensor combination are arranged at a plurality of locations in the target farmland, wherein the first sensor combination comprises: a temperature sensor, a wind sensor and a humidity sensor; Setting a plurality of second sensor combinations at a plurality of positions of the soil of the target farmland, wherein the second sensor combination comprises: a biosensor, a pH sensor, a temperature sensor and a humidity sensor; At multiple moments in the control cycle, images of farmland pests collected by various image acquisition devices are obtained; Determining the pest severity coefficient according to the farmland pest image; At multiple moments in the control cycle, environmental data collected by each first sensor combination is acquired, wherein the environmental data includes: temperature data, wind data and humidity data; At multiple moments in the control cycle, soil environmental data collected by each second sensor combination is obtained, wherein the soil environmental data includes: pesticide residue content, soil temperature data, soil moisture data and soil pH value data; Determining whether to use pesticides to control the pests based on the pest severity coefficient, the environmental data, and the soil environmental data; If it is determined to use pesticides to control pests, the amount of pesticides used is determined based on the pest severity coefficient and the soil environment data, and the pests are controlled within the control period based on the amount of pesticides used.

[0008] According to a second aspect of the present invention, there is provided an intelligent precision agriculture integrated control system, comprising: An environment detection device setting module is used to set a plurality of image acquisition devices and a first sensor combination at a plurality of locations in the target farmland, wherein the first sensor combination includes a temperature sensor, a wind sensor and a humidity sensor; A soil detection equipment setting module is used to set a plurality of second sensor combinations at a plurality of positions of the soil of the target farmland, wherein the second sensor combination includes: a biosensor, a pH sensor, a temperature sensor and a humidity sensor; The pest image acquisition module acquires the farmland pest images collected by each image acquisition device at multiple moments in the control cycle; An insect pest severity assessment module determines an insect pest severity coefficient based on the farmland insect pest image; An environmental data acquisition module is configured to acquire environmental data collected by each first sensor combination at multiple moments in a control cycle, wherein the environmental data includes: temperature data, wind data, and humidity data; A soil data acquisition module is used to acquire soil environmental data collected by each second sensor combination at multiple moments in the control cycle, wherein the soil environmental data includes: pesticide residue content, soil temperature data, soil moisture data and soil pH value data; A pesticide use judgment module determines whether to use pesticides to control the pests according to the pest severity coefficient, the environmental data and the soil environmental data; The pesticide dosage control module determines the pesticide dosage according to the pest severity coefficient and the soil environment data if it is determined to use pesticides to control pests, and controls the pests within a control period according to the pesticide dosage.

[0009] Technical effect: According to the present invention, the severity of pests can be accurately analyzed through farmland pest images, the pest severity coefficient can be determined, and environmental data and soil environmental data can be accurately obtained through sensor combination. Further, it is possible to judge whether to use pesticides based on the pest severity coefficient, environmental data and soil environmental data, and control the amount of pesticides, thereby improving the comprehensiveness and accuracy of agricultural pest control. When determining the pest severity coefficient, the pest severity coefficient can be determined based on the size of the pests, the degree of pest damage and the number of pests. During the calculation process, the size of the pests can be evaluated based on the number and size of the pests. Further, the severity of the pests of the pests can be evaluated based on the size of the pests and the degree of pest damage, thereby improving the comprehensiveness and accuracy of the pest severity coefficient. When judging whether to use pesticides, it is possible to determine whether to use pesticides to control pests based on the pest severity coefficient, environmental data and soil environmental data. During the calculation process, the influence of the severity of the pests, the pesticide residue status and the environmental factors on the use of pesticides is fully considered, thereby improving the accuracy of judging whether to use pesticides to control pests. When determining the pesticide dosage function, the pesticide dosage function can be determined based on historical pesticide dosage, historical soil environmental data and historical pest severity coefficients, accurately describing the relationship between pesticide dosage and pest severity coefficient, soil pH data, soil temperature data and soil moisture data, thereby improving the comprehensiveness and accuracy of the pesticide dosage function.

[0010] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and do not limit the present invention. Other features and aspects of the present invention will become more apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other embodiments can be obtained based on these drawings without creative work. Figure 1 A schematic diagram of a flow chart of an intelligent precision agriculture comprehensive control method according to an embodiment of the present invention is exemplarily shown; Figure 2 A block diagram of an intelligent precision agriculture integrated control system according to an embodiment of the present invention is exemplarily shown. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0013] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0014] Figure 1 A flow chart of an intelligent precision agriculture integrated control method according to an embodiment of the present invention is exemplarily shown, and the method includes: Step S101, setting a plurality of image acquisition devices and a first sensor combination at a plurality of locations in a target farmland, wherein the first sensor combination includes a temperature sensor, a wind sensor and a humidity sensor; Step S102, setting a plurality of second sensor combinations at a plurality of positions of the soil of the target farmland, wherein the second sensor combination comprises: a biosensor, a pH sensor, a temperature sensor and a humidity sensor; Step S103, acquiring farmland pest images collected by various image acquisition devices at multiple moments in the control cycle; Step S104, determining the pest severity coefficient according to the farmland pest image; Step S105, acquiring environmental data collected by each first sensor combination at multiple moments in the control cycle, wherein the environmental data includes: temperature data, wind data and humidity data; Step S106, acquiring soil environmental data collected by each second sensor combination at multiple moments in the control cycle, wherein the soil environmental data includes: pesticide residue content, soil temperature data, soil moisture data and soil pH value data; Step S107, determining whether to use pesticides to control the pests according to the pest severity coefficient, the environmental data and the soil environmental data; Step S108, if it is determined to use pesticides to control pests, the amount of pesticides is determined according to the pest severity coefficient and the soil environment data, and the pests are controlled within the control period according to the amount of pesticides.

[0015] According to the intelligent precision agriculture integrated control method of the embodiment of the present invention, the severity of pests can be accurately analyzed through farmland pest images, the pest severity coefficient can be determined, and environmental data and soil environmental data can be accurately acquired through a combination of sensors. Furthermore, whether to use pesticides can be judged based on the pest severity coefficient, environmental data and soil environmental data, and the amount of pesticides can be controlled, thereby improving the comprehensiveness and accuracy of agricultural pest control.

[0016] According to one embodiment of the present invention, in step S101, a plurality of image acquisition devices and a first sensor combination are set at a plurality of locations in the target farmland, wherein the first sensor combination includes a temperature sensor, a wind sensor and a humidity sensor.

[0017] For example, multiple image acquisition devices and first sensor combinations are evenly arranged at multiple locations in the farmland to collect images, temperature, wind force and humidity of the target farmland, and the number of image acquisition devices is the same as the number of first sensor combinations.

[0018] According to one embodiment of the present invention, in step S102, a plurality of second sensor combinations are set at a plurality of positions of the soil of the target farmland, wherein the second sensor combination includes: a biosensor, a pH sensor, a temperature sensor and a humidity sensor.

[0019] For example, multiple second sensor combinations are evenly set at multiple positions in the soil of the target farmland to collect the soil environmental conditions of the target farmland, and the biosensors are used to collect the pesticide residue conditions in the soil, wherein the positions of the first sensor combination and the corresponding second sensor combination are the same in the vertical direction, that is, the first sensor combination is set directly above the second sensor combination, and the number of the first sensor combination and the second sensor combination is the same.

[0020] According to an embodiment of the present invention, in step S103, images of farmland pests collected by various image collection devices are obtained at multiple moments in the control cycle.

[0021] For example, image acquisition equipment is used to acquire images of insect pests in farmland.

[0022] According to an embodiment of the present invention, in step S104, the pest severity coefficient is determined based on the farmland pest image.

[0023] According to one embodiment of the present invention, step S104 includes: In the farmland pest image, the pest type, pest size and pest quantity are identified by an image detection model; Determine the degree of pest damage based on the type of pest; The pest severity coefficient is determined based on the size of the pests, the degree of damage caused by the pests and the number of the pests.

[0024] For example, the image detection model is a type of deep learning model. The image detection model is trained through historical data so that it can identify the type, size and number of pests in the image. Different types of pests have different degrees of damage to agricultural crops. According to the identified pest types, combined with historical experience and reference materials, the degree of pest damage of each type of pest is determined. According to the size, number and degree of damage of the pests, the severity of the pest is evaluated and the pest severity coefficient is determined.

[0025] According to an embodiment of the present invention, the pest severity coefficient is determined according to the size of the pest, the degree of harm caused by the pest and the number of the pests, including: determining the pest severity coefficient of the rth image acquisition device at the i-th moment of the control cycle according to formula (1): , ; in, and is the preset weight, is the number of pests of the kth type at the i-th moment of the control cycle of the r-th image acquisition device, To preset the pest population threshold, is the damage degree of the kth pest at the rth image acquisition device, is the pest size of the jth pest of the kth type at the rth image acquisition device, is the preset pest size threshold, m is the number of pests of the kth type, K is the number of pest types, and K are both positive integers.

[0026] According to one embodiment of the present invention, is the relative difference between the number of pests of the kth type of pests at the i-th moment of the control cycle of the r-th image acquisition device and the preset pest number threshold. The larger the ratio, the greater the number of pests of the kth type of pests at the i-th moment of the control cycle of the r-th image acquisition device relative to the preset pest number, and the larger the scale of the pest; is the average size of the kth type of pests at the rth image acquisition device; is the relative difference between the average value of the size of the kth type of pests at the rth image acquisition device and the preset pest size threshold. The larger the ratio is, the larger the size of the kth type of pests at the rth image acquisition device is, and the larger the scale of the pest is. is a weighted sum based on the number and size of the k-th type of pests, representing the scale of pest infestation of the k-th type of pests; is the pest severity coefficient of the kth type of pest determined according to the pest scale and damage degree of the kth type of pest; The sum is calculated based on the number of pest types, which represents the pest severity coefficient of the rth image acquisition device at the i-th moment in the control cycle. The larger the value, the higher the severity of the pest of the rth image acquisition device at the i-th moment in the control cycle.

[0027] In this way, the pest severity coefficient can be determined based on the size of the pests, the degree of pest damage and the number of pests. During the calculation process, the size of the pests can be evaluated based on the number and size of the pests. Further, the severity of the pest damage can be evaluated based on the size of the pests and the degree of pest damage, which improves the comprehensiveness and accuracy of the pest severity coefficient.

[0028] According to an embodiment of the present invention, in step S105, environmental data collected by each first sensor combination is acquired at multiple moments in the control cycle, wherein the environmental data includes: temperature data, wind data and humidity data.

[0029] For example, at multiple moments in the control cycle, the temperature data, wind data, and humidity data collected by each first sensor combination are obtained, and the collected temperature data, wind data, and humidity data are used to determine whether the current environment is suitable for using pesticides for pest control.

[0030] According to one embodiment of the present invention, in step S106, soil environmental data collected by each second sensor combination is obtained at multiple times in the control cycle, wherein the soil environmental data includes: pesticide residue content, soil temperature data, soil moisture data and soil pH value data.

[0031] For example, at multiple moments in the control cycle, the pesticide residue content, soil temperature data, soil moisture data, and soil pH value data collected by each second sensor combination are obtained.

[0032] According to an embodiment of the present invention, in step S107, it is determined whether to use pesticides to control the pests based on the pest severity coefficient, the environmental data and the soil environmental data.

[0033] According to one embodiment of the present invention, step S107 includes: determining an environmental condition vector according to the temperature data, the wind force data and the humidity data; According to formula (2), the hazard degree condition A, pesticide residue condition B and environmental factor condition C are determined.

[0034] in, is the pest severity coefficient of the rth image acquisition device at the i-th moment in the control cycle, To preset the pest severity coefficient threshold, is the pesticide residue content of the rth second sensor combination at the i-th moment in the control cycle, To preset the pesticide residue threshold, To preset the wind data threshold, is the wind force data of the rth first sensor combination at the i-th moment in the control cycle, To preset the humidity data threshold, is the humidity data of the rth first sensor combination at the i-th moment of the control cycle, is the preset temperature data threshold, is the temperature data of the rth first sensor combination at the i-th moment of the control cycle, is the environmental condition vector of the rth first sensor combination at the i-th moment of the control cycle, for The transposed vector of ; When the hazard degree condition A, the pesticide residue condition B and the environmental factor condition C are all met, determining to use pesticides to control the pests; When the hazard level condition A is met and one of the pesticide residue condition B and the environmental factor condition C is not met, other methods are used to control the pests.

[0035] According to one embodiment of the present invention, in the hazard level condition A, , indicating that the pest severity coefficient of the rth image acquisition device at the i-th moment in the control cycle is greater than or equal to the preset pest severity coefficient threshold, indicating that the severity of the pest at this location is serious and measures need to be taken to control the pest.

[0036] According to one embodiment of the present invention, in the pesticide residue condition B, , indicating that the pesticide residue content of the rth first sensor combination at the i-th moment of the control cycle is less than the preset pesticide residue content threshold, indicating that the current pesticide residue content is small. When the pesticide residue content is greater than or equal to the preset pesticide residue content threshold, it means that the pesticide residue content at this position is too high. At this time, insect pests appear and the pests may have developed resistance. It is not appropriate to use pesticides to eliminate crops. Other methods should be used to control pests and remove pesticide residues to prevent high concentrations of pesticide residues from having a direct toxic effect on crops.

[0037] According to one embodiment of the present invention, in the environmental factor condition C, is the difference between the preset wind data threshold and the wind data of the rth first sensor combination at the i-th moment of the control cycle, when When it is less than 0, = -1, indicating that the wind data of the rth first sensor combination in the control cycle is too large. When the wind data is too large, the use of pesticides may cause the pesticides to remain in the crops for too short a time and fail to achieve the normal effect of eliminating pests. Therefore, when the wind data is too large, it is not appropriate to use pesticides for pest control, and other methods should be used for pest control; is the difference between the preset humidity data threshold and the humidity data of the rth first sensor combination at the i-th moment in the control cycle, When it is less than 0, = -1, indicating that the humidity data of the rth first sensor combination at the ith moment of the control cycle is too large. When the humidity of the air in the environment is too high, the amount of pesticide may be diluted and the normal effect of eliminating pests cannot be achieved. Therefore, when the humidity data is too high, it is not appropriate to use pesticides for pest control, and other methods should be used for pest control; is the difference between the preset temperature data threshold and the temperature data of the rth first sensor combination at the i-th moment of the control cycle, When it is less than 0, = -1, indicating that the temperature data of the rth first sensor combination at the ith moment of the control cycle is too large. When the temperature is too high, the pesticide may volatilize and fail to achieve the normal effect of eliminating pests. Therefore, when the temperature data is too large, it is not appropriate to use pesticides for pest control, and other methods should be used for pest control; is the environmental status vector of the rth first sensor combination at the i-th moment of the control cycle and The cosine similarity of the vectors, when the similarity is equal to 1, it means that the environmental conditions of the rth first sensor combination at the i-th moment in the control period are suitable for pest control by using pesticides. When the similarity is not equal to 1, it means that the environmental conditions of the rth first sensor combination at the i-th moment in the control period are not suitable for pest control by using pesticides.

[0038] In this way, whether to use pesticides to control pests can be determined based on the pest severity coefficient, environmental data and soil environmental data. During the calculation process, the impact of the severity of pests, pesticide residues and environmental factors on the use of pesticides is fully considered, which improves the accuracy of the judgment on whether to use pesticides to control pests.

[0039] According to one embodiment of the present invention, when the harm degree condition A is satisfied and there is an unsatisfied condition in the pesticide residue condition B and the environmental factor condition C, other methods are used to control the pests, including: When the hazard level condition A is met and there are unsatisfied conditions in the pesticide residue condition B and the environmental factor condition C, biological control and physical control are used to control pests.

[0040] For example, if the conditions for the degree of harm are met, but the conditions for pesticide residues and environmental factors are not met at the same time, it means that there are too many pesticide residues in the farmland or the environmental factors in the farmland do not meet the conditions for using pesticides to control pests (for example, strong winds or high humidity and temperature). At this time, biological control (for example, using predatory natural enemy insects to capture pests) or physical control (for example, using insect traps for trapping or insect nets for isolation) should be used to control pests.

[0041] According to one embodiment of the present invention, in step S108, if it is determined to use pesticides to control pests, the amount of pesticides is determined based on the pest severity coefficient and the soil environment data, and the pests are controlled within the control period based on the amount of pesticides.

[0042] According to one embodiment of the present invention, step S108 includes: Acquire historical pest severity coefficients, historical pesticide usage, and historical soil environmental data in historical control cycles, wherein the historical soil environmental data include: historical soil temperature data, historical soil moisture data, and historical soil pH value data; Determining a pesticide dosage function according to the historical pesticide dosage, the historical soil environment data and the historical pest severity coefficient; Determining the pesticide dosage according to the pesticide dosage function, the pest severity coefficient and the soil environment data; According to the pesticide dosage, the pests are controlled within the control period.

[0043] For example, historical pest severity coefficients, historical pesticide usage, and historical soil environmental data in multiple historical control cycles are obtained; based on the relationship between historical soil environmental data and historical pest severity coefficients corresponding to historical pesticide usage used in historical control cycles, a pesticide usage function is determined. Pesticide usage is related to pest severity and soil environmental conditions to a certain extent. For example, the more severe the pest, the greater the pesticide usage, and the more the soil environmental conditions conform to the pest growth environment, and more pesticide usage is needed to inhibit the growth of pests. The greater the pesticide usage, the pesticide usage function between historical pesticide usage and historical soil environmental data and historical pest severity coefficients can be determined based on the correlation of the above data; the pest severity coefficient and soil environmental data of the current control cycle are substituted into the pesticide usage function to determine the pesticide usage; based on the pest severity and soil environmental conditions, the dosage of pesticides to be used is determined, and pesticides are used to control pests to ensure that the dosage of pesticides used is not excessive and causes harm to human health, and at the same time, the dosage of pesticides used is not too small to prevent failure to achieve the purpose of pest elimination.

[0044] According to an embodiment of the present invention, determining a pesticide dosage function according to the historical pesticide dosage, the historical soil environment data and the historical pest severity coefficient includes: Determine a historical soil environment state vector according to the historical soil temperature data, the historical soil moisture data and the historical soil pH value data; According to formula (3), the equation to be fitted for the pesticide dosage function is determined.

[0045] in, and are the coefficients to be fitted, is the historical pesticide usage of the rth second sensor combination at the i-th moment in the a-th historical control cycle, is the historical pest severity coefficient of the rth image acquisition device at the i-th moment in the a-th historical control cycle, is the historical soil pH value data of the rth second sensor combination at the i-th moment in the a-th historical control cycle, is the historical soil temperature data of the rth second sensor combination at the i-th moment in the a-th historical control cycle, is the historical soil moisture data of the rth second sensor combination at the i-th moment in the a-th historical control cycle, is the historical soil environment state vector of the rth second sensor combination at the i-th moment in the a-th historical control cycle, for The transposed vector of To preset the soil pH data threshold, To preset the soil temperature threshold, To preset the soil moisture threshold, is the preset soil state vector; Solving the coefficient to be fitted according to the historical pesticide usage, the historical soil environment data and the historical insect pest severity coefficient to obtain a solution value of the coefficient to be fitted; The pesticide dosage function is obtained according to the solved values ​​of the coefficients to be fitted and the equation to be fitted.

[0046] According to one embodiment of the present invention, It means that the historical pesticide usage of the r-th second sensor combination at the i-th moment of the a-th historical control cycle is positively correlated with the historical pest severity coefficient of the r-th image acquisition device at the i-th moment of the a-th historical control cycle. The larger the historical pest severity coefficient, the larger the historical pesticide usage. and The preset soil pH data threshold, the preset soil moisture threshold and the preset soil humidity threshold represent the soil temperature, moisture and pH value suitable for the growth and reproduction of pests. It is the cosine similarity between the historical soil environment state vector of the rth second sensor combination at the i-th moment in the a-th historical control cycle and the preset soil state vector. The larger the similarity, the more suitable the historical soil environment state of the rth second sensor combination at the i-th moment in the a-th historical control cycle is for the survival of pests, and a relatively larger amount of pesticides is needed to inhibit the growth and reproduction of pests, indicating that the historical pesticide usage of the rth second sensor combination at the i-th moment in the a-th historical control cycle is positively correlated with the cosine similarity.

[0047] According to one embodiment of the present invention, fitting can be performed based on multiple parameters involved in the above equation to be fitted, that is, fitting can be performed based on the pest severity coefficient, historical soil pH value data, historical soil temperature data and historical soil moisture data to solve the above multiple coefficients to be fitted. There are 3 coefficients to be fitted, namely, and According to the insect pest severity coefficient, historical soil pH value data, historical soil temperature data and historical soil moisture data, the above three coefficients to be fitted are solved to obtain the solution values ​​of the above three coefficients to be fitted, and the solution values ​​of the above three coefficients to be fitted are substituted into the equation to be fitted to determine the pesticide dosage function.

[0048] In this way, the pesticide usage function can be determined based on historical pesticide usage, historical soil environmental data and historical pest severity coefficients, accurately describing the relationship between pesticide usage and pest severity coefficient, soil pH data, soil temperature data and soil moisture data, thereby improving the comprehensiveness and accuracy of the pesticide usage function.

[0049] According to the intelligent precision agriculture integrated control method of the embodiment of the present invention, the severity of pests can be accurately analyzed through the farmland pest image, the pest severity coefficient can be determined, and the environmental data and soil environmental data can be accurately obtained through the sensor combination. Further, it can be judged whether to use pesticides according to the pest severity coefficient, environmental data and soil environmental data, and the amount of pesticides can be controlled, thereby improving the comprehensiveness and accuracy of agricultural pest control. When determining the pest severity coefficient, the pest severity coefficient can be determined according to the size of the pests, the degree of pest damage and the number of pests. In the calculation process, the size of the pests can be evaluated according to the number and size of the pests. Further, the pest severity of the pests can be evaluated according to the size of the pests and the degree of pest damage, thereby improving the comprehensiveness and accuracy of the pest severity coefficient. When judging whether to use pesticides, it can be determined whether to use pesticides to control pests according to the pest severity coefficient, environmental data and soil environmental data. In the calculation process, the influence of the three aspects of pest severity, pesticide residue status and environmental factors on the use of pesticides is fully considered, thereby improving the accuracy of judging whether to use pesticides to control pests. When determining the pesticide dosage function, the pesticide dosage function can be determined based on historical pesticide dosage, historical soil environmental data and historical pest severity coefficients, accurately describing the relationship between pesticide dosage and pest severity coefficient, soil pH data, soil temperature data and soil moisture data, thereby improving the comprehensiveness and accuracy of the pesticide dosage function.

[0050] Figure 2 A block diagram of an intelligent precision agriculture integrated control system according to an embodiment of the present invention is exemplarily shown, and the system includes: An environment detection device setting module is used to set a plurality of image acquisition devices and a first sensor combination at a plurality of locations in the target farmland, wherein the first sensor combination includes a temperature sensor, a wind sensor and a humidity sensor; A soil detection equipment setting module is used to set a plurality of second sensor combinations at a plurality of positions of the soil of the target farmland, wherein the second sensor combination includes: a biosensor, a pH sensor, a temperature sensor and a humidity sensor; The pest image acquisition module acquires the farmland pest images collected by each image acquisition device at multiple moments in the control cycle; An insect pest severity assessment module determines an insect pest severity coefficient based on the farmland insect pest image; An environmental data acquisition module is configured to acquire environmental data collected by each first sensor combination at multiple moments in a control cycle, wherein the environmental data includes: temperature data, wind data, and humidity data; A soil data acquisition module is used to acquire soil environmental data collected by each second sensor combination at multiple moments in the control cycle, wherein the soil environmental data includes: pesticide residue content, soil temperature data, soil moisture data and soil pH value data; A pesticide use judgment module determines whether to use pesticides to control the pests according to the pest severity coefficient, the environmental data and the soil environmental data; The pesticide dosage control module determines the pesticide dosage according to the pest severity coefficient and the soil environment data if it is determined to use pesticides to control pests, and controls the pests within a control period according to the pesticide dosage.

[0051] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0052] It should be understood by those skilled in the art that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments, and the embodiments of the present invention may be deformed or modified in any way without departing from the principles.

Claims

1. An intelligent precision agriculture comprehensive control method, characterized in that: include: A plurality of image acquisition devices and a first sensor combination are arranged at a plurality of locations in the target farmland, wherein the first sensor combination comprises: a temperature sensor, a wind sensor and a humidity sensor; Setting a plurality of second sensor combinations at a plurality of positions of the soil of the target farmland, wherein the second sensor combination comprises: a biosensor, a pH sensor, a temperature sensor and a humidity sensor; At multiple moments in the control cycle, images of farmland pests collected by various image acquisition devices are obtained; Determining the pest severity coefficient according to the farmland pest image; At multiple moments in the control cycle, environmental data collected by each first sensor combination is acquired, wherein the environmental data includes: temperature data, wind data and humidity data; At multiple moments in the control cycle, soil environmental data collected by each second sensor combination is obtained, wherein the soil environmental data includes: pesticide residue content, soil temperature data, soil moisture data and soil pH value data; Determining whether to use pesticides to control the pests based on the pest severity coefficient, the environmental data, and the soil environmental data; If it is determined to use pesticides to control pests, the amount of pesticides used is determined based on the pest severity coefficient and the soil environment data, and the pests are controlled within the control period based on the amount of pesticides used.

2. The intelligent precision agriculture comprehensive control method according to claim 1, characterized in that: Determine the pest severity coefficient based on the farmland pest image, including: In the farmland pest image, the pest type, pest size and pest quantity are identified by an image detection model; Determine the degree of pest damage based on the type of pest; The pest severity coefficient is determined based on the size of the pests, the degree of damage caused by the pests and the number of the pests.

3. The intelligent precision agriculture comprehensive control method according to claim 2 is characterized in that: The pest severity coefficient is determined based on the size of the pest, the degree of damage caused by the pest and the number of the pest, including: According to the formula ； Determine the pest severity coefficient of the rth image acquisition device at the i-th moment of the control cycle ,in, and is the preset weight, is the number of pests of the kth type at the i-th moment of the control cycle of the r-th image acquisition device, To preset the pest population threshold, is the damage degree of the kth pest at the rth image acquisition device, is the pest size of the jth pest of the kth type at the rth image acquisition device, is the preset pest size threshold, m is the number of pests of the kth type, K is the number of pest types, and K are both positive integers.

4. The intelligent precision agriculture comprehensive control method according to claim 1, characterized in that: Determining whether to use pesticides to control the pests according to the pest severity coefficient, the environmental data and the soil environmental data includes: determining an environmental condition vector according to the temperature data, the wind force data and the humidity data; According to the formula ； Determine the hazard degree condition A, pesticide residue condition B and environmental factor condition C, among which, is the pest severity coefficient of the rth image acquisition device at the i-th moment in the control cycle, To preset the pest severity coefficient threshold, is the pesticide residue content of the rth second sensor combination at the i-th moment in the control cycle, To preset the pesticide residue threshold, To preset the wind data threshold, is the wind force data of the rth first sensor combination at the i-th moment in the control cycle, To preset the humidity data threshold, is the humidity data of the rth first sensor combination at the i-th moment of the control cycle, is the preset temperature data threshold, is the temperature data of the rth first sensor combination at the i-th moment of the control cycle, is the environmental condition vector of the rth first sensor combination at the i-th moment of the control cycle, for The transposed vector of ; When the hazard degree condition A, the pesticide residue condition B and the environmental factor condition C are all met, determining to use pesticides to control the pests; When the hazard level condition A is met and one of the pesticide residue condition B and the environmental factor condition C is not met, other methods are used to control the pests.

5. The intelligent precision agriculture comprehensive control method according to claim 4, characterized in that: When the hazard level condition A is met, and the pesticide residue condition B and the environmental factor condition C are not met, other methods are used to control the pests, including: When the hazard level condition A is met and there are unsatisfied conditions in the pesticide residue condition B and the environmental factor condition C, biological control and physical control are used to control pests.

6. The intelligent precision agriculture comprehensive control method according to claim 1, characterized in that: If it is determined to use pesticides to control pests, the amount of pesticides to be used is determined based on the pest severity coefficient and the soil environment data, and the pests are controlled within the control period based on the pesticide amount, including: Acquire historical pest severity coefficients, historical pesticide usage, and historical soil environmental data in historical control cycles, wherein the historical soil environmental data include: historical soil temperature data, historical soil moisture data, and historical soil pH value data; Determining a pesticide dosage function according to the historical pesticide dosage, the historical soil environment data and the historical pest severity coefficient; Determining the pesticide dosage according to the pesticide dosage function, the pest severity coefficient and the soil environment data; According to the pesticide dosage, the pests are controlled within the control period.

7. The intelligent precision agriculture comprehensive control method according to claim 6, characterized in that: Determining a pesticide dosage function according to the historical pesticide dosage, the historical soil environment data and the historical pest severity coefficient includes: Determine a historical soil environment state vector according to the historical soil temperature data, the historical soil moisture data and the historical soil pH value data; According to the formula ； Determine the equation to be fitted for the pesticide dosage function, where and are the coefficients to be fitted, is the historical pesticide usage of the rth second sensor combination at the i-th moment in the a-th historical control cycle, is the historical pest severity coefficient of the rth image acquisition device at the i-th moment in the a-th historical control cycle, is the historical soil pH value data of the rth second sensor combination at the i-th moment in the a-th historical control cycle, is the historical soil temperature data of the rth second sensor combination at the i-th moment in the a-th historical control cycle, is the historical soil moisture data of the rth second sensor combination at the i-th moment in the a-th historical control cycle, is the historical soil environment state vector of the rth second sensor combination at the i-th moment in the a-th historical control cycle, for The transposed vector of To preset the soil pH data threshold, To preset the soil temperature threshold, To preset the soil moisture threshold, is the preset soil state vector; Solving the coefficient to be fitted according to the historical pesticide usage, the historical soil environment data and the historical insect pest severity coefficient to obtain a solution value of the coefficient to be fitted; The pesticide dosage function is obtained according to the solved values ​​of the coefficients to be fitted and the equation to be fitted.

8. An intelligent precision agriculture integrated control system, characterized in that: include: An environment detection device setting module is used to set a plurality of image acquisition devices and a first sensor combination at a plurality of locations in the target farmland, wherein the first sensor combination includes a temperature sensor, a wind sensor and a humidity sensor; A soil detection equipment setting module is used to set a plurality of second sensor combinations at a plurality of positions of the soil of the target farmland, wherein the second sensor combination includes: a biosensor, a pH sensor, a temperature sensor and a humidity sensor; The pest image acquisition module acquires the farmland pest images collected by each image acquisition device at multiple moments in the control cycle; An insect pest severity assessment module determines an insect pest severity coefficient based on the farmland insect pest image; An environmental data acquisition module is configured to acquire environmental data collected by each first sensor combination at multiple moments in a control cycle, wherein the environmental data includes: temperature data, wind data, and humidity data; A soil data acquisition module is used to acquire soil environmental data collected by each second sensor combination at multiple moments in the control cycle, wherein the soil environmental data includes: pesticide residue content, soil temperature data, soil moisture data and soil pH value data; A pesticide use judgment module determines whether to use pesticides to control the pests according to the pest severity coefficient, the environmental data and the soil environmental data; The pesticide dosage control module determines the pesticide dosage according to the pest severity coefficient and the soil environment data if it is determined to use pesticides to control pests, and controls the pests within a control period according to the pesticide dosage.

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