LEAF DISEASE DETECTION SYSTEM USING MACHINE LEARNING

Abstract

This invention relates to an automated system for plant leaf disease detection utilizing deep learning architectures, specifically ResNet and Inception V4, to achieve over 95% accuracy. The system preprocesses and analyzes images of plant leaves, extracting significant features to classify diseases in real-time. Designed for use in agricultural fields, the system integrates with IoT devices for continuous monitoring and offers a mobile application interface for farmers and agronomists to receive immediate notifications and disease management insights. Optimized for low computational complexity, the system supports diverse plant species and provides a practical tool for improving crop yield and reducing losses.

Specification

Description:The present invention relates to an agricultural technology sector, and more specifically to the field of plant pathology, machine learning, and image processing. The invention provides an automated system for detecting, diagnosing, and classifying leaf diseases using deep learning algorithms, aiming to enhance agricultural productivity by enabling timely intervention and minimizing crop losses. The system leverages advanced image processing techniques, deep learning models like ResNet and Inception V4, and IoT integration for real-time disease monitoring and detection.
BACKGROUND OF THE INVENTION
Agriculture plays a vital role in India, supporting approximately 58% of the population's livelihood. The increasing population has led to a surge in food demand, necessitating efforts to maximize crop yields. A major obstacle to achieving higher yields is the prevalence of plant diseases, particularly those affecting high-demand crops like tomatoes. Tomato plants are susceptible to various diseases caused by microorganisms, viruses, and fungi, which can result in significant losses if not managed effectively.
Traditionally, farmers and agronomists have relied on manual inspection and expertise to identify and diagnose plant diseases. However, these methods are time-consuming, prone to human error, and often require specialized knowledge, making them impractical for large-scale implementation. As a result, there is a pressing need for automated solutions that can accurately detect leaf diseases and provide immediate, actionable insights.
By utilizing advanced machine learning techniques and deep learning architectures such as ResNet and Inception V4, this invention addresses these challenges. It offers a highly accurate, efficient, and scalable solution for identifying and classifying diseases in tomato plants and other crops, supporting farmers in making informed, timely decisions to protect their yield.

OBJECTIVE OF INVENTION
An object of the present invention is to develop a system that utilizes deep learning techniques, specifically ResNet and Inception V4 architectures, for high-accuracy classification of plant leaf diseases, targeting an accuracy rate above 95%.
Another object of the present invention is to automate the disease detection process to reduce manual intervention and optimize computational complexity, making the system suitable for rural and resource-constrained environments.
Yet another object of the present invention is to provide a scalable model capable of identifying and diagnosing a variety of leaf diseases in multiple plant species, with a primary focus on tomatoes.
Another object of the present invention is to provide real-time or near-real-time disease detection and classification, ensuring that farmers and agronomists receive timely and actionable insights for mitigating crop loss.
Another object of the present invention is to provide the system with IoT devices and mobile applications for easy accessibility and field deployment, enhancing the system's reach and effectiveness.
SUMMARY OF THE INVENTION
According to the present invention, proposes an advanced, automated leaf disease detection system using machine learning techniques, specifically deep learning architectures like ResNet and Inception V4. The system is designed to collect, preprocess, and analyze images of plant leaves for identifying diseases. The data acquisition process involves capturing high-resolution images of plant leaves, which are then normalized, resized, and augmented to improve data quality. The system extracts significant features from the images using deep learning algorithms, enabling the model to classify various diseases with high accuracy (over 95%).
To ensure efficient and real-time disease detection, the model is optimized through techniques such as model pruning and quantization, reducing computational overhead while maintaining accuracy. The invention is adaptable to various plant species, and it integrates with IoT devices for field deployment, making it accessible and practical for real-time agricultural applications. Farmers and agronomists can use the system through mobile applications to receive immediate notifications and visual analysis of disease conditions, enabling them to take timely action.
In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF DRAWINGS
An embodiment of this invention, illustrating its features, will now be described in detail. The words "comprising," "having," "containing," and "including," and other forms thereof are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items.
The terms "first," "second," and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another, and the terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item.
The present invention is an advanced automated system designed to detect, diagnose, and classify plant leaf diseases using deep learning and machine learning techniques. The system focuses on providing accurate, efficient, and scalable solutions to improve agricultural productivity, particularly for tomato plants, although it is adaptable to various other plant species. The system comprises multiple components and processes, each contributing to the overall functionality and effectiveness of the invention. The following is an in-depth explanation of each component and its function:
1. Data Acquisition:
The system uses high-resolution cameras integrated into IoT devices or mobile platforms to capture detailed images of plant leaves. These cameras are placed in agricultural fields to continuously monitor the crops, taking periodic images for analysis.
The system can capture a wide variety of images, including different angles, lighting conditions, and plant stages, ensuring that the dataset is diverse and representative of real-world scenarios. This variety enhances the system's robustness and its ability to generalize across different environments.
The acquired images are stored in a cloud-based database or local storage, depending on the deployment setup. This storage allows for easy access and retrieval for further processing and analysis.
2. Data Preprocessing:
Before the images are fed into the deep learning model, they undergo a comprehensive preprocessing phase to ensure consistency and quality. This includes several steps:
Normalization: The system adjusts the brightness and contrast levels of the images to standardize the visual appearance, making it easier for the model to recognize patterns regardless of environmental factors.
Resizing: All images are resized to a fixed dimension (e.g., 224x224 pixels) to maintain uniformity and reduce computational load, making it suitable for real-time processing.
Data Augmentation: Techniques such as rotation, flipping, cropping, and zooming are applied to create variations in the dataset. Augmentation helps the model learn from diverse scenarios, reducing the risk of overfitting and improving its ability to generalize to unseen images.
Noise Reduction: The system employs filtering techniques like Gaussian blur to remove unwanted noise and enhance the clarity of leaf features, making it easier for the deep learning model to identify disease symptoms accurately.
3. Feature Extraction and Selection:
The system utilizes deep learning models like ResNet (Residual Network) and Inception V4 to extract significant features from the preprocessed images. These models are chosen for their efficiency in image classification tasks and their ability to handle complex patterns.
ResNet architecture helps in learning deep and complex features from the dataset by using residual connections, which minimize the vanishing gradient problem. This allows the model to extract intricate details such as leaf texture, vein structure, and lesion patterns, which are critical for identifying specific diseases.
Inception V4 architecture is used as an alternative or complementary model to increase accuracy. It utilizes multiple convolutional layers that operate at different scales, enabling the model to capture both fine details and broader patterns simultaneously.
The system employs feature selection algorithms that prioritize the most relevant features, such as color changes, shape anomalies, and texture variations. This enhances the model's efficiency and accuracy by focusing only on the essential aspects of the image.
4. Model Training and Optimization:
The system is trained using a large, annotated dataset comprising thousands of images of both healthy and diseased leaves from multiple plant species, with a focus on tomatoes. The dataset includes various disease categories, such as bacterial spots, early blight, and leaf mold, providing a comprehensive training base for the model.
The deep learning model undergoes supervised training, where images and their corresponding disease labels are used to teach the model to recognize and classify diseases accurately. The training process includes:
Hyperparameter Tuning: Parameters such as learning rate, batch size, number of epochs, and optimizer type (e.g., Adam or SGD) are adjusted to maximize model performance and minimize loss.
Validation and Testing: A portion of the dataset is set aside for validation and testing to evaluate the model's accuracy and robustness. The model's performance is monitored using metrics like accuracy, precision, recall, and F1 score.
The system incorporates optimization techniques like model pruning, where redundant neurons and layers are removed, and quantization, which reduces the precision of the model weights. These techniques minimize the model's size and computational requirements without sacrificing accuracy, making it suitable for deployment on devices with limited resources.
5. Disease Classification and Real-time Detection:
Once trained, the model is deployed to classify and diagnose leaf diseases in real-time or near-real-time. The system uses IoT devices, such as smart cameras or drones, to capture images of crops and analyze them immediately.
The classification process involves feeding the captured images into the trained model, which then predicts the type of disease (if any) present in the leaf. The system provides detailed diagnostic information, including the disease name, severity level, and suggested interventions.
The real-time capability of the system allows farmers and agronomists to receive immediate alerts and actionable insights, enabling them to respond quickly to prevent the spread of diseases and minimize crop loss. The system can also integrate with farm management software to provide recommendations for pesticide application or other agricultural practices based on the detected disease.
6. System Deployment and User Interface:
The system can be deployed through various platforms, including:
IoT Devices: Cameras installed in agricultural fields continuously monitor plant health and capture images that are analyzed on-site or transmitted to a cloud server for further processing.
Mobile Applications: The system offers a user-friendly mobile application that farmers and agronomists can use to upload images of diseased leaves for immediate analysis. The app provides visual diagnostics, reports, and recommended treatments.
The user interface is designed to be intuitive, offering visual indicators (e.g., green for healthy, red for diseased) and text-based information describing the disease and necessary steps to manage it. The system also stores historical data, allowing users to track disease trends and patterns over time.
7. Adaptability and Scalability:
While the system is primarily designed for tomato plants, it is adaptable to various other crops by retraining the model with additional datasets relevant to those plants. The system supports multiple plant species and can be customized to target specific diseases of interest.
The architecture is scalable, meaning that it can be deployed in small farms as well as large agricultural enterprises. The cloud-based system supports scaling up as data volume increases, ensuring that the system remains efficient regardless of the deployment size.
8. Integration with Agricultural Systems:
The system can be integrated with existing agricultural monitoring systems and platforms for seamless operation. It can connect with farm management software, weather monitoring systems, and irrigation controllers to provide a comprehensive solution for crop health management.
For instance, if the system detects a high incidence of a specific disease, it can automatically trigger pest control systems or adjust irrigation schedules to optimize conditions for disease prevention.
9. Computational Efficiency and Resource Management:
To ensure the system remains efficient and cost-effective, several measures are taken to manage computational resources:
Edge Computing: By using edge devices, such as smart cameras and IoT sensors, the system processes data at the source, reducing latency and bandwidth requirements. Only relevant information is sent to the cloud, conserving network resources.
Energy Efficiency: The system is designed to operate with low power consumption, making it suitable for rural and remote areas where power supply may be limited. Solar-powered IoT devices can be used to enhance energy efficiency and sustainability.
This detailed description illustrates how the invention provides a comprehensive, scalable, and efficient solution for real-time leaf disease detection and classification using advanced machine learning techniques. It is designed to improve agricultural productivity by enabling early disease detection and intervention, ultimately contributing to higher crop yields and reduced losses
The foregoing descriptions of specific embodiments of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present invention to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present invention, and its practical application to thereby enable others skilled in the art to best utilize the present invention and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omission and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the spirit or scope of the claims of the present invention.
, Claims:1. An automated plant leaf disease detection system, comprising:
a data acquisition module configured to capture high-resolution images of plant leaves using IoT-enabled cameras or mobile platforms,
a) a preprocessing unit that normalizes, resizes, augments, and applies noise reduction to the captured images,
b) a deep learning model utilizing ResNet architecture for extracting significant features from the pre-processed images and classifying them based on disease type,
c) an output interface providing real-time diagnostic information, including the identified disease type and recommended interventions.

2. The A method for identifying plant leaf diseases, comprising the steps of:
d) collecting high-resolution images of plant leaves using integrated IoT cameras,
e) preprocessing the collected images by normalizing, resizing, augmenting, and filtering noise,
f) applying a deep learning model based on ResNet architecture to extract key features from the preprocesses images,
g) classifying the disease type and providing diagnostic information and suggested interventions through a user interface accessible via a mobile application.

3. The automated plant leaf disease detection system as claimed in claim 1, wherein the deep learning model further includes an Inception V4 architecture, used alternatively or in conjunction with the ResNet model, to enhance the accuracy and robustness of disease classification.

4. The automated plant leaf disease detection system as claimed in claim 1, wherein the data acquisition module includes a variety of imaging devices, such as drones and fixed cameras, to capture images from different angles and in various lighting conditions, ensuring comprehensive data collection.

5. The automated plant leaf disease detection system as claimed in claim 1, wherein the preprocessing unit includes an augmentation process that rotates, flips, and zooms the images to increase dataset variability, thereby improving model accuracy and generalization capabilities.

6. The automated plant leaf disease detection system as claimed in claim 1, wherein the output interface integrates with IoT devices in agricultural fields, providing continuous monitoring, disease alerts, and automatic integration with farm management software.

7. The automated plant leaf disease detection system as claimed in claim 1, wherein the output interface is designed as a mobile application, which provides visual diagnostics and disease management reports that are accessible to farmers and agronomists in real-time.

8. The automated plant leaf disease detection system as claimed in claim 1, wherein the system is adaptable for multiple plant species, allowing retraining of the deep learning model with datasets specific to various crops, thereby expanding its application beyond tomato plants.

9. The automated plant leaf disease detection system as claimed in claim 3, wherein the deep learning models are trained using a diverse dataset that includes images under varying environmental conditions and from different growth stages of the plants to improve the robustness and reliability of disease detection.

10. The method as claimed in claim 2, further including a step for optimizing the deep learning model using pruning and quantization techniques to reduce computational overhead and increase processing speed while maintaining classification accuracy.

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