Intelligent agricultural management system

Abstract

The invention relates to an intelligent agricultural management system. The system comprises an electronic eye camera, geometric correction equipment, customized processing equipment, direction filtering equipment, an image identification mechanism and a dynamic management mechanism. The system can increase the intelligence level of agricultural processing.

Description

Technical Field

[0001] The present invention relates to the field of smart agriculture, and more specifically, to a smart agriculture management system. Background Art

[0002] Smart agriculture is to apply the Internet of Things technology to traditional agriculture, using sensors and software to control agricultural production through mobile platforms or computer platforms, making traditional agriculture more "intelligent". In addition to precise perception, control and decision-making management, in a broad sense, smart agriculture also includes agricultural e-commerce, food traceability and anti-counterfeiting, agricultural leisure tourism, agricultural information services and other aspects.

[0003] The so-called "smart agriculture" is to make full use of the achievements of modern information technology, integrate the application of computer and network technology, Internet of Things technology, audio and video technology, 3S technology, wireless communication technology and expert wisdom and knowledge, and realize intelligent management such as agricultural visualization remote diagnosis, remote control, and disaster warning.

[0004] For example, CN119168566A relates to the field of smart agriculture technology, and in particular to a smart agriculture system based on big data. This technology is equipped with an inspection module, a regular distribution recognition module, an image extraction module, and a model analysis module. The inspection module is used to collect images of the target area, and specific continuous texture segments are identified based on chromaticity. The distribution regularity characterization coefficient is calculated based on the number of specific continuous texture segments and the angle difference between the specific continuous texture segments, and the regular distribution category of the target area image is divided. According to the regular distribution category, the texture difference analysis and chromaticity analysis of the target area image are performed, and abnormal local images are identified and transmitted to the model analysis module, and the pest and disease characteristics in the abnormal local images transmitted by the image extraction module are identified. This technology can accurately and quickly identify pest and disease characteristics, avoid repeated traversal, avoid waste of computing power, and improve the computing speed.

[0005] CN119107034A provides a smart agriculture integration system based on the Internet of Things and digital twins, which relates to the field of data collection technology, including: an Internet of Things sensing network, which is used to collect data on soil moisture, soil oxygen, environmental parameters, meteorological information, and crop growth status; a big data processing unit, which receives data collected by sensors and feeds the processed data back to a digital twin model; establishes a digital twin model, and constructs a digital twin model of farmland through computer learning methods and deep learning algorithms, and uses real-time data and historical data of farmland; a control and decision-making unit, which formulates farmland management strategies and decisions based on the analysis and prediction results of the digital twin model; a remote monitoring and management platform, where users remotely monitor and manage the operating status of farmland through terminals. The present invention realizes the informatization and intelligence of the entire chain of agricultural production through the Internet of Things and digital twin technologies, and improves the efficiency and quality of agricultural production. Summary of the invention

[0006] In order to solve the technical problems in the prior art, the present invention provides a smart agricultural management system, which can, whenever a directional filtered image is received, use the image with the smallest mean square error of pixel values ​​in multiple frames of past directional filtered images received as the latest background image, subtract the latest background image from the latest received directional filtered image to obtain a corresponding difference image, perform set type pest detection processing in the difference image to determine whether there are set type pests and the number of set type pests, and determine the preparation dose of pesticide for spraying the set farmland area based on the number of set type pests and the coverage area of ​​the set farmland area, wherein the determined preparation dose of pesticide for spraying the set farmland area is positively correlated with the number of set type pests, and the determined preparation dose of pesticide for spraying the set farmland area is positively correlated with the coverage area of ​​the set farmland area, thereby avoiding waste of pesticides while ensuring the insecticidal effect of the set type pests.

[0007] To this end, the present invention needs to have at least the following three important invention points: Invention point A: When each directional filter image is received, an image with the smallest mean square error of pixel values ​​among the received multiple frames of past directional filter images is used as the latest background image, and the latest background image is subtracted from the latest directional filter image to obtain a corresponding difference image, and a set type of pest detection process is performed in the difference image to determine whether there are set type of pests and the number of set type of pests; Invention point B: The image with the smallest mean square error of pixel values ​​among the received multiple frames of past directional filtered images is used as the latest background image, including: for each frame of image, obtaining the mean square error of each pixel value corresponding to each component pixel point of the image as the mean square error of the pixel value corresponding to the image; Invention point C: The preparation dosage of pesticide for spraying the set farmland area is determined based on the number of set type of pests and the coverage area of ​​the set farmland area, wherein the determined preparation dosage of pesticide for spraying the set farmland area is positively correlated with the number of set type of pests, and the determined preparation dosage of pesticide for spraying the set farmland area is positively correlated with the coverage area of ​​the set farmland area, thereby avoiding waste of pesticides while ensuring the insecticidal effect of the set type of pests.

[0008] According to the present invention, a smart agriculture management system is provided, the system comprising: The electronic eye camera is arranged just above the center of the set farmland area, and is used to complete a single camera action of the set farmland area based on a shooting trigger instruction received by a built-in network interface to obtain a corresponding farmland area picture; A geometric correction device, which is arranged on the smart agriculture server side and is wirelessly connected to the electronic eye camera, is used to perform geometric correction processing on the received farmland area picture to obtain and output a corresponding geometric correction image; a custom processing device connected to the geometric correction device, for performing adaptive recursive filtering processing on the received geometrically corrected image to obtain and output a corresponding recursive filtered image; A directional filtering device, connected to the customized processing device, for performing directional filtering processing on the received recursively filtered image to obtain and output a corresponding directional filtered image; An image recognition mechanism is provided at the end of a smart agriculture server and connected to the directional filtering device, a variance analysis unit, a difference processing unit and a target identification unit, wherein the difference processing unit is respectively connected to the mean square error analysis unit and the target identification unit, and the image recognition mechanism is used to, when each directional filtering image is received, use the image with the smallest mean square error value of pixel values ​​among the received multiple frames of past directional filtering images as the latest background image, subtract the latest background image from the latest received directional filtering image to obtain a corresponding difference image, and perform a set type of pest detection processing in the difference image to determine whether a set type of pest exists and the number of the set type of pests, wherein the step of subtracting the latest background image from the latest received directional filtering image to obtain a corresponding difference image, and performing a set type of pest detection processing in the difference image to determine whether a set type of pest exists and the number of the set type of pests includes: performing a set type of pest detection processing in the difference image based on the appearance imaging characteristics corresponding to the set type of pest to determine whether a set type of pest exists and the number of the set type of pests; A dynamic management mechanism, connected to the image recognition mechanism, is used to determine a preparation dosage of pesticides to be sprayed on a set farmland area based on the number of set types of pests present and the coverage area of ​​the set farmland area; Among them, determining the preparation dose of pesticides for spraying the set farmland area based on the number of set types of pests and the coverage area of ​​the set farmland area includes: the determined preparation dose of pesticides for spraying the set farmland area is positively correlated with the number of set types of pests, and the determined preparation dose of pesticides for spraying the set farmland area is positively correlated with the coverage area of ​​the set farmland area. DETAILED DESCRIPTION

[0009] Example 1

[0010] According to embodiment 1 of the present invention, a smart agriculture management system is shown, and the system includes: The electronic eye camera is arranged just above the center of the set farmland area, and is used to complete a single camera action of the set farmland area based on a shooting trigger instruction received by a built-in network interface to obtain a corresponding farmland area picture; For example, the electronic eye camera has a built-in imaging lens, a command receiving unit and a camera execution unit, which are used to complete a single camera action of the set farmland area based on the shooting trigger command received by the built-in network interface to obtain the corresponding farmland area picture; A geometric correction device, which is arranged on the smart agriculture server side and is wirelessly connected to the electronic eye camera, is used to perform geometric correction processing on the received farmland area picture to obtain and output a corresponding geometric correction image; a custom processing device connected to the geometric correction device, for performing adaptive recursive filtering processing on the received geometrically corrected image to obtain and output a corresponding recursive filtered image; A directional filtering device, connected to the customized processing device, for performing directional filtering processing on the received recursively filtered image to obtain and output a corresponding directional filtered image; An image recognition mechanism is provided at the end of a smart agriculture server and connected to the directional filtering device, a variance analysis unit, a difference processing unit and a target identification unit, wherein the difference processing unit is respectively connected to the mean square error analysis unit and the target identification unit, and the image recognition mechanism is used to, when each directional filtering image is received, use the image with the smallest mean square error value of pixel values ​​among the received multiple frames of past directional filtering images as the latest background image, subtract the latest background image from the latest received directional filtering image to obtain a corresponding difference image, and perform a set type of pest detection processing in the difference image to determine whether a set type of pest exists and the number of the set type of pests, wherein the step of subtracting the latest background image from the latest received directional filtering image to obtain a corresponding difference image, and performing a set type of pest detection processing in the difference image to determine whether a set type of pest exists and the number of the set type of pests includes: performing a set type of pest detection processing in the difference image based on the appearance imaging characteristics corresponding to the set type of pest to determine whether a set type of pest exists and the number of the set type of pests; A dynamic management mechanism, connected to the image recognition mechanism, is used to determine a preparation dosage of pesticides to be sprayed on a set farmland area based on the number of set types of pests present and the coverage area of ​​the set farmland area; Wherein, determining the preparation dosage of the pesticide to be sprayed on the set farmland area based on the number of the set type of pests and the coverage area of ​​the set farmland area includes: the determined preparation dosage of the pesticide to be sprayed on the set farmland area is positively correlated with the number of the set type of pests, and the determined preparation dosage of the pesticide to be sprayed on the set farmland area is positively correlated with the coverage area of ​​the set farmland area; Among them, based on the appearance imaging characteristics corresponding to the set type of pests, the set type of pests detection processing is performed in the difference image to determine whether there are set type of pests and the number of set type of pests, including: the set type of pests are pest types that infringe the corresponding crops planted in the set farmland area.

[0011] Example 2

[0012] A smart agriculture management system is shown according to Embodiment 2 of the present invention.

[0013] Different from Example 1, the smart agricultural management system in Example 2 may also include the following components: a voltage conversion device, arranged near the geometric correction device, the custom processing device, the directional filtering device and the image recognition mechanism and connected to the geometric correction device, the custom processing device, the directional filtering device and the image recognition mechanism respectively; Among them, the voltage conversion device is arranged near the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism and is respectively connected to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism, including: the voltage conversion device is used to provide the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism with the required working voltages respectively.

[0014] Example 3

[0015] Embodiment 3 of the present invention shows a smart agriculture management system.

[0016] Different from Example 1, the smart agriculture management system in Example 3 may also include the following components: A quartz oscillator device is arranged near the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism and is connected to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively; Wherein, the quartz oscillator device is arranged near the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism and is respectively connected to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism, and comprises: the quartz oscillator device is used to provide the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism with the reference clock pulses required by each of them; wherein a programmable logic device is used to perform image data processing on the output data of the geometric correction device, the custom processing device, the directional filtering device and the image recognition mechanism to obtain output processing data corresponding to the geometric correction device, the custom processing device, the directional filtering device and the image recognition mechanism respectively; Among them, using a programmable logic device to perform image data processing on the output data of the geometric correction device, the custom processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the custom processing device, the directional filtering device and the image recognition mechanism respectively includes: performing image sharpening processing on the output data of the geometric correction device, the custom processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the custom processing device, the directional filtering device and the image recognition mechanism respectively; Among them, using a programmable logic device to perform image data processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively includes: performing image enhancement processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively; Among them, using a programmable logic device to perform image data processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively includes: performing image filtering processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively; Among them, a programmable logic device is used to perform image data processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively, including: performing distortion correction processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively.

[0017] In addition, in the smart agriculture management system, the electronic eye camera is arranged directly above the central position of the set farmland area, and is used to complete a single camera action of the set farmland area based on the shooting trigger instruction received by the built-in network interface to obtain the corresponding farmland area picture, including: the built-in network interface of the electronic eye camera is wirelessly connected to the smart agriculture server.

[0018] The smart agricultural management system of the present invention is intelligent in operation and widely used. Since the preparation dosage of the pesticide to be sprayed on the set farmland area can be determined based on the number of the set type of pests and the coverage area of ​​the set farmland area, the determined preparation dosage of the pesticide to be sprayed on the set farmland area is positively correlated with the number of the set type of pests and the coverage area of ​​the set farmland area, thereby avoiding the waste of pesticides while ensuring the insecticidal effect of the set type of pests.

[0019] Although the preferred embodiments of the present invention describe the present invention, it is understood that other similar embodiments may be used, or modifications and additions may be made to the described embodiments to perform the same functions of the present invention without departing from the present invention. For example, although the exemplary embodiments of the present invention are described in the context of a digital device that emulates the functions of a personal computer, those skilled in the art will recognize that the present invention is not limited to such digital devices, and as described in this application, the present invention may be applied to any number of existing or emerging computing devices or environments. Therefore, the present invention should not be limited to any single embodiment, but rather should be interpreted in accordance with the breadth and scope of the appended claims.

Claims

1. A smart agricultural management system, characterized in that: The system comprises: The electronic eye camera is arranged just above the center of the set farmland area, and is used to complete a single camera action of the set farmland area based on a shooting trigger instruction received by a built-in network interface to obtain a corresponding farmland area picture; A geometric correction device, which is arranged on the smart agriculture server side and is wirelessly connected to the electronic eye camera, is used to perform geometric correction processing on the received farmland area picture to obtain and output a corresponding geometric correction image; a custom processing device connected to the geometric correction device, for performing adaptive recursive filtering processing on the received geometrically corrected image to obtain and output a corresponding recursive filtered image; A directional filtering device, connected to the customized processing device, for performing directional filtering processing on the received recursively filtered image to obtain and output a corresponding directional filtered image; An image recognition mechanism is provided at the end of a smart agriculture server and connected to the directional filtering device, a variance analysis unit, a difference processing unit and a target identification unit, wherein the difference processing unit is respectively connected to the mean square error analysis unit and the target identification unit, and the image recognition mechanism is used to, when each directional filtering image is received, use the image with the smallest mean square error value of pixel values ​​among the received multiple frames of past directional filtering images as the latest background image, subtract the latest background image from the latest received directional filtering image to obtain a corresponding difference image, and perform a set type of pest detection processing in the difference image to determine whether a set type of pest exists and the number of the set type of pests, wherein the step of subtracting the latest background image from the latest received directional filtering image to obtain a corresponding difference image, and performing a set type of pest detection processing in the difference image to determine whether a set type of pest exists and the number of the set type of pests includes: performing a set type of pest detection processing in the difference image based on the appearance imaging characteristics corresponding to the set type of pest to determine whether a set type of pest exists and the number of the set type of pests; A dynamic management mechanism, connected to the image recognition mechanism, is used to determine a preparation dosage of pesticides to be sprayed on a set farmland area based on the number of set types of pests present and the coverage area of ​​the set farmland area; Among them, determining the preparation dose of pesticides for spraying the set farmland area based on the number of set types of pests and the coverage area of ​​the set farmland area includes: the determined preparation dose of pesticides for spraying the set farmland area is positively correlated with the number of set types of pests, and the determined preparation dose of pesticides for spraying the set farmland area is positively correlated with the coverage area of ​​the set farmland area.

2. The smart agricultural management system according to claim 1, characterized in that: Performing a set type of pest detection process in the difference image based on the appearance imaging characteristics corresponding to the set type of pest to determine whether there is a set type of pest and the number of the set type of pests includes: the set type of pest is a pest type that infringes on crops corresponding to a set farmland area.

3. The smart agricultural management system according to claim 2, characterized in that: The system further comprises: a voltage conversion device, arranged near the geometric correction device, the custom processing device, the directional filtering device and the image recognition mechanism and connected to the geometric correction device, the custom processing device, the directional filtering device and the image recognition mechanism respectively; Among them, the voltage conversion device is arranged near the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism and is respectively connected to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism, including: the voltage conversion device is used to provide the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism with the required working voltages respectively.

4. The smart agricultural management system according to claim 2, characterized in that: The system further comprises: A quartz oscillator device is arranged near the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism and is connected to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively; Among them, the quartz oscillator device is arranged near the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism and is respectively connected to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism, including: the quartz oscillator device is used to provide the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism with the reference clock pulses they need respectively.

5. The smart agricultural management system according to any one of claims 2 to 4, characterized in that: A programmable logic device is used to perform image data processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively.

6. The smart agricultural management system according to claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively includes: performing image sharpening processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively.

7. The smart agricultural management system according to claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively includes: performing image enhancement processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively.

8. The smart agricultural management system according to claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively includes: performing image filtering processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively.

9. The smart agricultural management system according to claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively includes: performing distortion correction processing on the output data of the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism to obtain the output processing data corresponding to the geometric correction device, the customized processing device, the directional filtering device and the image recognition mechanism respectively.