Intelligent identification weed prevention and control system

Abstract

The invention relates to the technical field of image recognition, and discloses an intelligent recognition weed prevention and control system. The system comprehensively obtains images and environmental parameters of field crops and weeds through a data acquisition unit by using an unmanned aerial vehicle and a ground camera in combination with an environmental sensor, an image analysis unit carries out denoising and enhancement processing on the acquired images, and precise weed recognition and positioning are realized through a deep learning convolutional neural network. The weed recognition and classification unit classifies the types and growth states of weeds according to analysis results, the prevention and control strategy making unit automatically generates a targeted prevention and control scheme according to the growth characteristics and environmental conditions of the weeds and sends out control instructions, and the strategy execution unit drives a pesticide spraying and mechanical weeding system according to the instructions. Precise operation is achieved, the monitoring unit tracks the system state in real time in the whole execution process, and therefore the purposes of reducing weeding labor cost and labor intensity and optimizing agricultural resource allocation are achieved.

Description

Technical Field

[0001] This invention relates to the field of image recognition technology, specifically to an intelligent weed control system. Background Technology

[0002] With the continuous advancement of agricultural modernization, the production efficiency and quality of crops are receiving increasing attention. Against this backdrop, weeds, as an important factor affecting crop growth, pose a serious threat to agricultural production. Weeds not only compete with crops for water and nutrients, but may also become breeding grounds for pests and diseases, directly affecting the yield and quality of crops. Therefore, how to effectively identify and control weeds has become an important topic in modern agricultural research.

[0003] Traditional weed control methods mainly rely on manual observation and weeding, which is not only labor-intensive but also inefficient and makes it difficult to achieve precise management. Summary of the Invention

[0004] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides an intelligent weed control system. The system utilizes a data acquisition unit with drones and ground cameras, combined with environmental sensors, to comprehensively acquire images and environmental parameters of crops and weeds in the field, providing foundational data for subsequent analysis. An image analysis unit denoises and enhances the acquired images, extracting color, texture, and shape features. Deep learning convolutional neural networks are then used to achieve accurate weed identification and location. A weed identification and classification unit categorizes weeds by type and growth status based on the analysis results, providing crucial information for developing control strategies. A control strategy formulation unit automatically generates targeted control plans based on weed growth characteristics and environmental conditions, issuing control commands. A strategy execution unit then drives the spraying and mechanical weeding systems according to these commands, achieving precise operation. A monitoring unit tracks the system status in real time throughout the entire process and provides convenient remote monitoring and data analysis functions via a mobile terminal. The application of this system not only reduces labor costs and intensity but also optimizes resource allocation, laying the foundation for sustainable agricultural development.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: an intelligent weed identification and control system, comprising a data acquisition unit, an image analysis unit, a weed identification and classification unit, a control strategy formulation unit, a strategy execution unit, and a monitoring unit; The data acquisition unit is used to acquire site images of crops and weeds and environmental parameters in the field area through drones, ground cameras and environmental sensors. The image analysis unit is used to receive field images of crops and weeds in the field area collected by the data acquisition unit, perform image denoising and image enhancement, extract color features, texture features and shape features of the image, perform feature encoding and analysis of the image based on deep learning convolutional neural network, and calculate the weed recognition confidence level to determine the probability of the presence of weeds in the field area. The weed identification and classification unit is used to receive the analysis results of the image analysis unit, further distinguish and classify the weeds, and determine the reproductive stage and growth status of the weeds. The control strategy formulation unit sets differentiated control strategies based on the weed reproduction stage, weed growth status, and environmental parameters, automatically formulates weed control plans, and generates control instructions for the execution of the weed control plans. The strategy execution unit drives the spraying system and the mechanical weeding system based on the control instructions of the weed control plan to complete the precise spraying and mechanical weeding operations in the field area. The monitoring unit is used to monitor the working status of the spraying system and the mechanical weeding system in real time during the strategy execution process, and provides a mobile-based remote monitoring, data analysis and management interface.

[0006] Preferably, the formula for image denoising is as follows: ; In the formula, This represents the image obtained after denoising. This represents the image data to be denoised. This represents the smoothed image produced by applying Gaussian blur to the original image. This represents the denoising intensity coefficient, with a value between 0 and 1.

[0007] Preferably, the formula for image enhancement is as follows: ; In the formula, This indicates an image that has undergone enhancement processing. This indicates that the image after denoising is sharpened. This represents the contribution coefficient that controls the original image. This represents the contribution coefficient to controlling sharpening.

[0008] Preferably, the formula used to extract the color features of the image is as follows: ; In the formula, Represents color feature values. Indicates the number of color channels. Indicates the first The average pixel values ​​in each color channel Indicates the index subscript.

[0009] Preferably, the formula used for extracting texture features is as follows: ; In the formula, Indicates pixel position Pointed Encoded values ​​used to describe local textures. Indicates the number of neighboring pixels. Indicates the first The grayscale value of each neighboring pixel. This represents the grayscale value of the center pixel. This represents a binary function.

[0010] Preferably, the formula used for extracting shape features is as follows: ; In the formula, This represents the area of ​​the weed outline. This represents the count of all pixels in the outline. A set representing the outline pixels of weeds. Indicates the first in the outline Two-dimensional coordinates of a pixel Indicates horizontal position. The vertical position is represented by each pixel, which is counted as 1 in the area unit. The total number of pixels is obtained by summing the values.

[0011] Preferably, the formula for feature encoding of the image is as follows: ; In the formula, The extracted feature vector is a high-dimensional quantitative description of the image's features, used for classification and recognition. This represents a deep convolutional neural network model that automatically extracts high-level features. This represents the preprocessed weed image data.

[0012] Preferably, the formula for calculating the confidence level of weed identification is as follows: ; In the formula, This represents the confidence probability that the current area is weeds. The closer the value is to 1, the more likely it is to be weeds. This represents the unnormalized score for the corresponding weed category. This represents the unnormalized score for the corresponding crop category.

[0013] Preferably, the reproductive stage of the weeds is determined by the following formula: ; In the formula, This indicates the reproductive stage of the exported weeds. Indicates lower order, Indicates intermediate level, Indicates higher order, This indicates the proportion of the area covered by weeds in the entire tested area. This indicates a low threshold during the reproductive stage. This indicates a high threshold during the reproductive stage.

[0014] Preferably, the growth status of the weeds is calculated using the following formula: ; In the formula, Indicates the growth rate of weeds. This indicates the area of ​​weeds currently detected. This indicates the area of ​​weeds at the start of the detection. This indicates the time elapsed from the initial test to the current test.

[0015] Compared with existing technologies, the present invention provides an intelligent weed identification and control system, which has the following beneficial effects: This invention utilizes a data acquisition unit with drones and ground cameras, combined with environmental sensors, to comprehensively acquire images and environmental parameters of crops and weeds in the field, providing foundational data for subsequent analysis. An image analysis unit denoises and enhances the acquired images, extracting color, texture, and shape features. Deep learning convolutional neural networks enable precise weed identification and location. A weed identification and classification unit categorizes weeds by type and growth status based on the analysis results, providing crucial information for developing control strategies. A control strategy development unit automatically generates targeted control plans based on weed growth characteristics and environmental conditions, issuing control commands. A strategy execution unit then drives the spraying and mechanical weeding systems according to these commands, achieving precise operation. A monitoring unit tracks the system status in real time throughout the entire process and provides convenient remote monitoring and data analysis functions via a mobile terminal. The application of this system not only reduces labor costs and intensity but also optimizes resource allocation, laying the foundation for sustainable agricultural development. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system flow of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Traditional weed control methods rely primarily on manual observation and weeding, which is labor-intensive, inefficient, and hinders precise management. Therefore, an intelligent weed identification and control system is proposed. Please refer to [link / reference]. Figure 1 The system includes a data acquisition unit, an image analysis unit, a weed identification and classification unit, a prevention and control strategy formulation unit, a strategy execution unit, and a monitoring unit. The data acquisition unit mainly relies on a drone platform, a high-precision ground camera system, and a variety of environmental sensors. Combined with multi-source information acquisition technology, it achieves efficient and comprehensive field monitoring in the field area. The drones are equipped with high-resolution, multispectral, and multi-angle imaging equipment. Utilizing autonomous flight or autonomous navigation capabilities, they periodically or in real-time collect high-quality image data of crops and weeds, providing detailed spatial information for crop production management. Ground cameras capture high-definition images of local areas of the plot in a fixed or mobile manner, playing a particularly important role in key growth stages or when identifying weeds. In conjunction with environmental sensors, such as temperature, humidity, light, soil moisture, and nutrient status sensors, it comprehensively monitors field climate and soil conditions, enabling dynamic tracking of environmental parameters. These multi-source, multi-angle images and environmental data, after being processed by an efficient data acquisition, transmission, and storage system, combined with wireless communication technology and cloud platform processing capabilities, provide a solid data foundation for subsequent image analysis, weed identification, precision pesticide application, and scientific fertilization, significantly improving the level of intelligent agricultural management and crop yield assurance capabilities. The image analysis unit is responsible for receiving and processing high-resolution field images of crops and weeds in the field area collected by the data acquisition unit. Relying on image processing technology, a multi-level data preprocessing process ensures the reliability of image quality. Specific measures include denoising the original images using techniques such as Gaussian blurring or median filtering to reduce random noise caused by flight vibrations or environmental interference, thereby improving the accuracy of subsequent analysis. Subsequently, image enhancement techniques, such as sharpening and histogram equalization, are used to enhance image details and contrast, making subtle features clearer and easier to extract. Next, by extracting color features (such as RGB average value, color histogram, etc.), texture features (such as Local Binary Pattern (LBP), gray-level co-occurrence matrix), and shape features (such as contour area, perimeter, aspect ratio, roundness, etc.), the spatial characteristics of weeds and crops are comprehensively described, providing a rich feature base for subsequent identification. These features are then encoded and analyzed by a deep learning convolutional neural network (CNN) model to automatically extract key discriminative information, greatly improving the model's discriminative ability. Specific feature encoding formulas are as follows: ; This indicates the use of pre-trained or custom-trained convolutional neural networks to process the input image. Feature extraction is performed to obtain high-level feature vectors with discriminative power. To accurately identify weeds, the system also calculates the confidence level of weeds according to the following classification probability formula: ; in, and These represent the unnormalized scores of the model for the "weeds" and "crops" categories, respectively, and calculate the probability of weed presence. This helps to achieve accurate weed monitoring and management decisions. Introducing these technologies and formulas significantly improves the accuracy and efficiency of field weed detection, provides reliable data support for automated weeding and intelligent management, effectively reduces labor costs in agricultural production, and improves crop yield and quality. The weed identification and classification unit primarily relies on advanced machine learning and deep learning algorithms. It receives weed feature information from the image analysis unit and performs accurate species identification and classification. This unit uses a trained classification model, such as a convolutional neural network (CNN) or other deep learning architecture, to classify weeds into different species (e.g., broadleaf weeds, grasses, etc.) using the input multi-dimensional feature vectors. This enables multi-category, multi-dimensional identification and management. The specific classification process can employ a softmax classifier to determine the probability that a particular weed belongs to a specific category. ; in, Indicates the first The classification score (logits) corresponding to the class. This represents the total number of categories. Using this probability, different types of weeds can be identified efficiently and accurately, providing a foundation for subsequent precise application of pesticides and management. Regarding the determination of weed reproduction stages, the system analyzes characteristic data such as weed area ratio, number of offspring, stem thickness, and number of leaves. Using threshold judgment or classification models, it calculates the weed reproduction stage, such as low-level reproduction, intermediate reproduction, or high-level reproduction, to help formulate targeted weed control strategies. The corresponding reproduction stage determination formula is as follows: ; in, This represents the ratio of the area covered by weeds to the total area of ​​the tested region. , In addition to the thresholds for dividing the reproductive stages, the weed identification and classification unit also conducts multi-dimensional assessments based on indicators such as the number of weed leaves, stem thickness, and color changes to determine its growth status (e.g., germination stage, vigorous growth stage, and decline stage), which facilitates precise application of pesticides and control. By combining these technologies and indicators, the weed identification and classification unit can achieve refined management of weeds in the field, thereby effectively improving the efficiency of agricultural resource utilization, reducing the use of chemical agents, and ultimately promoting the sustainable development of agriculture. The control strategy formulation unit, based on multi-source real-time data integration and intelligent decision-making technology, integrates weed reproduction stages, current weed growth status, and field environmental parameters (such as soil moisture, temperature, light intensity, wind speed, and air humidity monitoring data). Utilizing big data analytics, rule engines, and expert system integration technologies, it can perform dynamic, multi-objective optimization and intelligent reasoning to automatically generate personalized and differentiated weed control plans. In its implementation, machine learning models (such as random forests, support vector machines, or deep learning models) are used to predict and classify weed growth parameters. Combined with environmental parameters, the unit assesses the weed's reproductive potential and growth risk, thereby formulating differentiated management strategies for different reproduction and growth stages. These strategies cover the selection of different types and dosages of herbicides, and the appropriate routes for mechanized operations. By adjusting the parameters of the path planning, irrigation control scheme, and electronic spraying system, and utilizing the intelligent decision support system (DSS) and automatic control algorithms, the system can analyze and integrate multi-source data in real time according to the preset strategy model, automatically formulate the optimal or feasible control plan, and transform specific operation instructions into a standardized set of control instructions (such as spraying start, spraying amount, spraying time, mechanical operation path, automatic irrigation scheduling, etc.). These control instructions are directly transmitted to the execution equipment through wireless communication modules or industrial control interfaces (such as PLC, CAN bus, etc.), realizing fully automated, multi-device collaborative operation, thereby significantly improving the timeliness and accuracy of weed control, effectively reducing the amount of chemical agents used and the cost of mechanical operation, while minimizing the impact on the field ecological environment, and achieving the goal of intelligent, green, and efficient agricultural management. The strategy execution unit, through an embedded control system and industrial automation technology, directly receives control commands from the weed control plan generated by the decision-making unit. This precisely drives the operation of the spraying system and mechanical weeding equipment. Specifically, it uses high-speed execution controllers (such as PLCs or industrial controllers) to adjust the opening and closing of spraying valves, the spraying volume, and the spraying time, as well as to schedule the path, speed, and operating mode of the mechanical weeding equipment. This ensures accurate and efficient weeding operations within the pre-defined area. Simultaneously, the monitoring unit utilizes Internet of Things (IoT) technology and wireless communication modules (such as 4G / 5G, Wi-Fi, LoRa, or industrial fieldbus) to collect real-time data on the spraying and weeding system. The system collects operational status information, including key parameters such as spraying flow rate, spraying pressure, machinery operating status, and equipment fault alarms. By building a monitoring and management system based on a cloud platform or edge computing, it provides a remote monitoring and management interface with multi-dimensional data analysis, fault diagnosis, and historical record functions. Users can view the equipment's operating status and work progress in real time, remotely schedule and adjust work parameters, and achieve remote control and management through mobile applications or web pages. This series of technical means not only ensures the automation and high precision of operations, but also enhances the system's reliability and remote controllability, effectively improves the level of intelligent agricultural production management, reduces labor costs, and achieves real-time and traceability of field management.

[0019] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent weed control system, characterized in that: It includes a data acquisition unit, an image analysis unit, a weed identification and classification unit, a control strategy formulation unit, a strategy execution unit, and a monitoring unit; The data acquisition unit is used to acquire site images of crops and weeds and environmental parameters in the field area through drones, ground cameras and environmental sensors. The image analysis unit is used to receive field images of crops and weeds in the field area collected by the data acquisition unit, perform image denoising and image enhancement, extract color features, texture features and shape features of the image, perform feature encoding and analysis of the image based on deep learning convolutional neural network, and calculate the weed recognition confidence level to determine the probability of the presence of weeds in the field area. The weed identification and classification unit is used to receive the analysis results of the image analysis unit, further distinguish and classify the weeds, and determine the reproductive stage and growth status of the weeds. The control strategy formulation unit sets differentiated control strategies based on the weed reproduction stage, weed growth status, and environmental parameters, automatically formulates weed control plans, and generates control instructions for the execution of the weed control plans. The strategy execution unit drives the spraying system and the mechanical weeding system based on the control instructions of the weed control plan to complete the precise spraying and mechanical weeding operations in the field area. The monitoring unit is used to monitor the working status of the spraying system and the mechanical weeding system in real time during the strategy execution process, and provides a mobile-based remote monitoring, data analysis and management interface.

2. The intelligent weed control system according to claim 1, characterized in that: The formula for image denoising is as follows: ； In the formula, This represents the image obtained after denoising. This represents the image data to be denoised. This represents the smoothed image produced by applying Gaussian blur to the original image. This represents the denoising intensity coefficient, with a value between 0 and 1.

3. The intelligent weed control system according to claim 2, characterized in that: The formula for image enhancement is as follows: ； In the formula, This indicates an image that has undergone enhancement processing. This indicates that the image after denoising is sharpened. This represents the contribution coefficient that controls the original image. This represents the contribution coefficient to controlling sharpening.

4. The intelligent weed control system according to claim 3, characterized in that: The formula used to extract the color features of the image is as follows: ； In the formula, Represents color feature values. Indicates the number of color channels. Indicates the first The average pixel values ​​in each color channel Indicates the index subscript.

5. The intelligent weed control system according to claim 4, characterized in that: The formula used to extract texture features is shown below: ； In the formula, Indicates pixel position Pointed Encoded values ​​used to describe local textures. Indicates the number of neighboring pixels. Indicates the first The grayscale value of each neighboring pixel. This represents the grayscale value of the center pixel. This represents a binary function.

6. The intelligent weed control system according to claim 5, characterized in that: The formula used to extract shape features is shown below: ； In the formula, This represents the area of ​​the weed outline. This represents the count of all pixels in the outline. A set representing the outline pixels of weeds. Represents the first in the contour Two-dimensional coordinates of a pixel Indicates horizontal position. The vertical position is represented by each pixel, which is counted as 1 in the area unit. The total number of pixels is obtained by summing the values.

7. The intelligent weed control system according to claim 6, characterized in that: The formula for feature encoding of the image is as follows: ； In the formula, The extracted feature vector is a high-dimensional quantitative description of the image's features, used for classification and recognition. This represents a deep convolutional neural network model that automatically extracts high-level features. This represents the preprocessed weed image data.

8. The intelligent weed control system according to claim 7, characterized in that: The formula for calculating the confidence level of weed identification is as follows: ； In the formula, This represents the confidence probability that the current area is weeds. The closer the value is to 1, the more likely it is to be weeds. This represents the unnormalized score for the corresponding weed category. This represents the unnormalized score for the corresponding crop category.

9. The intelligent weed control system according to claim 8, characterized in that: The reproductive stage of the weeds is determined by the following formula: ； In the formula, This indicates the reproductive stage of the exported weeds. Indicates lower order, Indicates intermediate level, Indicates higher order, This indicates the proportion of the area covered by weeds in the entire tested area. This indicates a low threshold during the reproductive stage. This indicates a high threshold during the reproductive stage.

10. The intelligent weed control system according to claim 9, characterized in that: The growth status of the weeds is calculated using the following formula: ； In the formula, Indicates the growth rate of weeds. This indicates the area of ​​weeds currently detected. This indicates the area of ​​weeds at the start of the detection. This indicates the time elapsed from the initial test to the current test.

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