Deep learning-based image processing system for the automated detection of pests and diseases in crops

Abstract

An automated system for the detection of pests and diseases in crops using image processing based on deep learning, which includes the following: ◯ an image acquisition unit (101) configured to capture high-resolution images of crops; ◯ a preprocessing unit (102) for improving image quality by applying noise reduction, contrast enhancement and segmentation techniques; ◯ a convolutional neural network (CNN) model(103) trained on a dataset of annotated images of pests and diseases and configured to extract features and classify detected anomalies; ◯ a decision unit (104) that, based on the detected pests or diseases, generates diagnostic reports and provides treatment recommendations; ◯ an integrated mobile (105) and web-based application interface that provides real-time analytics, historical data tracking and recommendations for farmers and agricultural experts.

Description

Field of the invention:

[0001] The present invention relates to the field of agricultural engineering, in particular to an automated system for detecting pests and diseases in crops using deep learning-based image processing. Background:

[0002] Monitoring crop health is an important aspect of modern agriculture. Conventional methods for pest and disease detection involve manual inspections, which are time-consuming, labor-intensive, and prone to human error. These methods often rely on visual assessments by experts or farmers, leading to inconsistencies in detection accuracy due to variations in human perception, environmental conditions, and disease stages.

[0003] In recent years, the agricultural sector has seen a significant increase in the adoption of precision agriculture techniques to improve productivity and sustainability. Technological advances in imaging sensors, machine learning, and artificial intelligence have paved the way for automatic pest and disease detection systems. By leveraging deep learning models trained on large-scale datasets, these systems can provide early warning of pest infestations and disease outbreaks, thus minimizing crop losses and reducing the overuse of chemical treatments.

[0004] Deep learning, particularly convolutional neural networks (CNNs), have achieved remarkable success in image classification and pattern recognition. In agriculture, CNNs can analyze crop images captured by drones, satellites, or ground-based cameras to identify anomalies, classify diseases, and detect pest infestations with high accuracy. Furthermore, integrating deep learning with IoT (Internet of Things) sensors and real-time data processing can improve decision-making and enable farmers to take targeted actions to optimize resource use.

[0005] Despite these technological advances, challenges remain for the widespread adoption of automated pest and disease detection systems. Factors such as variability in environmental conditions, differences in crop species, and variations in disease symptoms require robust and adaptable models. Furthermore, computational efficiency and affordability are critical factors for the deployment of such systems in resource-limited agricultural environments.

[0006] The present invention addresses these challenges by proposing a deep learning-based image processing system tailored for real-time pest and disease detection. The system integrates high-resolution images, advanced machine learning algorithms, and a user-friendly interface to provide farmers with actionable insights for effective crop management. By automating the detection process, this system increases agricultural productivity, reduces dependence on chemical pesticides, and promotes sustainable farming practices. Objective of the invention:

[0007] The present invention discloses a system and method for automated pest and disease detection in crops using a deep learning-based image processing system. The invention uses an image acquisition unit, a processing unit employing convolutional neural networks (CNNs), and a decision-making unit to provide real-time analysis and recommendations for pest and disease control.

[0008] This system captures high-resolution images of crops using an imaging device, such as a drone-mounted camera or a stationary soil sensor. These images are then processed using a deep learning algorithm trained on a diverse dataset containing images of various plant diseases and pest infestations. The trained CNN model extracts features such as color variations, texture patterns, and shape anomalies to identify and classify potential threats to plant health.

[0009] Once the analysis is complete, the system generates real-time alerts and treatment recommendations based on the severity and type of the detected problem. The decision-making unit integrates with farm management software, enabling seamless implementation of corrective actions, such as the precise application of pesticides or biological control methods. Furthermore, the system offers a mobile and web-based user interface that allows farmers and agricultural experts to access diagnostic reports, visualizations, and historical data trends.

[0010] One of the key innovations of this invention is its ability to operate in diverse agricultural environments, adapting to different crop types and regional differences in pest and disease prevalence. The deep learning model is continuously updated through active learning to ensure it remains accurate and effective in detecting emerging threats. The system can also be integrated with IoT sensors to enhance environmental monitoring and provide a comprehensive approach to crop health management.

[0011] By automating pest and disease detection, this invention significantly reduces manual labor, minimizes economic losses due to undetected infestations, and promotes environmentally sustainable agriculture by optimizing pesticide use. Furthermore, large-scale implementation of this system can contribute to global food security by improving agricultural efficiency and resilience to climate-related pest and disease outbreaks. Short description of the drawing Fig. : shows a block diagram of the system according to the invention. Detailed description:

[0012] The invention consists of a deep learning-based image processing system that effectively identifies and classifies pests and diseases in crops and provides recommendations for their control. The system is designed to operate autonomously or in conjunction with existing agricultural monitoring instruments to improve the efficiency and precision of crop protection.

[0013] The system begins with an image acquisition unit, which can include high-resolution cameras on drones, stationary ground-based cameras, or mobile imaging devices. These imaging units capture real-time images of crops under various lighting and environmental conditions. Multispectral imaging techniques are used to improve accuracy, allowing the system to detect disease symptoms that may not be visible to the naked eye.

[0014] Once the images are captured, they undergo a preprocessing phase where noise reduction, contrast enhancement, and segmentation techniques are applied to improve image quality. This step is critical to ensure the deep learning model obtains clear, well-defined images that maximize classification accuracy.

[0015] The processed images are then analyzed using a CNN (convolutional neural network) model trained on a large dataset of annotated pest and disease images. The CNN model uses multiple layers of feature extraction to detect anomalies in color, texture, shape, and other key indicators of pest infestation and plant disease. The model is continuously improved using machine learning techniques, incorporating new images and real-world data to increase accuracy over time.

[0016] After the analysis is complete, the system classifies the detected anomalies into specific pest or disease categories. This classification process allows the system to identify the type of infestation or disease affecting the crop, as well as its severity. By comparing the extracted characteristics with an extensive database of known pest and disease patterns, the system delivers highly reliable results with minimal false alarms.

[0017] The decision unit then creates a report containing the diagnostic results, severity, and recommended treatments. These recommendations are based on historical data, environmental factors, and best practices for pest and disease control. For example, if a fungal disease is detected, the system can suggest an appropriate fungicide treatment, as well as the optimal application timing and dosage. If an insect infestation is detected, the system can recommend biological control methods or the targeted use of pesticides to minimize crop damage.

[0018] Furthermore, the system can be integrated with Internet of Things (IoT) devices such as soil moisture sensors and weather stations to enable a comprehensive assessment of plant health. By combining real-time environmental data with deep learning-based image analysis, the system enhances its predictive capabilities, enabling farmers to take preventative measures before widespread crop damage occurs.

[0019] To ensure accessibility and usability, the system includes a mobile and web-based application interface that allows farmers and agricultural experts to access real-time reports, view image analysis results, and track pest and disease trends over time. This user-friendly interface enables decision-makers to quickly implement effective pest control strategies that reduce crop losses and improve overall agricultural productivity.

[0020] This invention represents a significant advance in precision agriculture, providing a scalable and cost-effective solution for farmers of all sizes. By automating pest and disease detection, the system reduces the need for manual inspections, increases accuracy, and promotes sustainable agricultural practices by optimizing pesticide and fungicide use. Furthermore, the ability to integrate into existing farm management systems makes this invention a valuable addition to modern farms, helping farmers improve their efficiency and yields.

[0021] The present invention provides an efficient and automated solution for detecting pests and diseases in crops using deep learning-based image processing. This system significantly improves agricultural productivity by reducing human intervention, increasing accuracy, and enabling timely pest control measures.

[0022] The deep learning-based image processing system for automated detection of pests and diseases in crops combines high-resolution image processing, artificial intelligence analysis, and real-time decision-making. The invention is designed to work seamlessly across diverse agricultural environments, providing farmers with immediate insights to prevent potential crop losses.

[0023] The system begins with an image capture unit that captures high-resolution images of crops using drone-mounted cameras, stationary imaging units, or mobile devices. These images serve as raw data for the system, ensuring a comprehensive view of the crop fields. The imaging technology also includes multispectral and hyperspectral capabilities, enabling the detection of early-stage infections and pest infestations that may not be visible to the naked eye.

[0024] After the images are captured, they are preprocessed to remove noise, adjust brightness and contrast, and segment the relevant plant parts to improve analysis accuracy. This ensures that the images fed into the deep learning model contain only relevant data, minimizing false positives and improving system reliability. The preprocessed images are then fed into a CNN (convolutional neural network) model trained on large-scale datasets containing labeled images of various plant diseases and pest infestations. The CNN processes the images layer by layer, extracting key features such as leaf discoloration, texture variations, and abnormal structural patterns to classify them into specific disease or pest categories.

[0025] The system also features a decision-making unit that interprets the results of the CNN model and provides real-time feedback to the farmer. The severity of the detected problem is assessed, and actionable recommendations are generated. These recommendations can include the targeted use of pesticides, alternative biological control methods, or preventative measures to stop the spread of a detected disease. The results are then communicated via an integrated mobile and web-based interface for access by farmers, agronomists, and agricultural stakeholders.

[0026] To illustrate the practical implementation of the system, let's consider a real-life example: A farmer growing tomatoes in a large field notices signs of leaf discoloration and suspects a possible disease outbreak. Instead of manually examining each individual plant, the farmer uses a drone equipped with the imaging system to scan the entire field. The drone captures high-resolution images and sends them to the deep learning-based processing unit for analysis. The system detects early symptoms of late blight—a fungal disease that spreads rapidly and can lead to severe yield losses. Within minutes, the system generates an alert with details about the affected areas, the severity of the infection, and recommended treatment options, such as:the application of a specific fungicide or the removal of infected plants to prevent further spread.

[0027] If the farmer implements the recommendations promptly, they can curb crop losses, reduce unnecessary pesticide use, and improve overall field productivity. Furthermore, the farmer can use the system's historical data to track recurring disease patterns and implement long-term prevention strategies.

[0028] The system continuously learns and improves through active machine learning techniques. As more images and real-world data are processed, the CNN model updates itself to detect emerging pests and diseases with greater accuracy. The integration of IoT sensors further enhances detection capabilities by incorporating environmental factors such as humidity, temperature, and soil conditions, enabling a holistic approach to plant health monitoring.

[0029] This invention significantly reduces dependence on manual surveying, improves the accuracy of early detection, and optimizes the management of pests and diseases in agricultural fields. By automating the process and leveraging artificial intelligence, the system ensures sustainable farming practices, economic efficiency, and improved food security for farming communities worldwide. List of reference symbols 100 systems 101 Image acquisition unit 102 Preprocessing unit 103 Model of a convolutional neural network (CNN) 104 Decision-Making Unit 105 integrated mobile

Claims

[1] An automated system for detecting pests and diseases in crops using deep learning-based image processing, comprising: ◯ an image acquisition unit (101) configured to acquire high-resolution images of crops; ◯ a pre-processing unit (102) for improving image quality by applying noise reduction, contrast enhancement and segmentation techniques; ◯ a convolutional neural network (CNN) model(103) trained on a dataset of annotated images of pests and diseases and configured to extract features and classify detected anomalies; ◯ a decision-making unit (104) which prepares diagnostic reports and makes treatment recommendations based on the pests or diseases detected; ◯ an integrated mobile (105) and web-based application interface that provides real-time analytics, historical data tracking, and recommendations for farmers and agricultural experts. [2] The system of claim 1, wherein the image acquisition unit comprises drone-mounted cameras, stationary ground-based cameras, or mobile imaging devices capable of acquiring multispectral images. [3] The system of claim 1, wherein the CNN model is continuously updated by machine learning-based active learning techniques to improve detection accuracy for emerging pests and diseases. [4] The system of claim 1, wherein the decision-making unit is integrated with farm management software and IoT-based sensors to incorporate environmental and weather-related data into the analysis. [5] The system of claim 1, wherein the generated treatment recommendations include optimal application strategies for pesticides or biological control agents based on historical pest and disease trends. [6] The system of claim 1, wherein the mobile and web-based application provides users with real-time alerts, data visualizations, and predictive analytics for improved crop management decision making.

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