- PROBLEM DOMAIN:
The problem domain of the "Plant Disease Detection" project revolves around addressing the critical challenges faced by the agricultural sector in identifying and managing plant diseases. Agriculture is a fundamental component of global food production, and the health of crops directly influences food security. However, the prevalence of plant diseases poses a significant threat to crop yields and quality. Traditional methods of disease detection often rely on visual inspections by farmers, leading to delayed identification and response. This project aims to leverage artificial intelligence and machine learning techniques to automate and enhance the detection process, providing an efficient and timely solution for identifying plant diseases. By focusing on this problem domain, the project seeks to contribute to sustainable agriculture practices, minimizing crop losses, and ensuring the overall health and productivity of plant species crucial for human sustenance. Plant diseases are a significant issue for farmers everywhere. They may potentially result in plant death and result in major agricultural losses. Visual inspection and other traditional methods of plant disease identification need a lot of time and effort. Additionally, they are not always reliable, particularly when it comes to diseases that can be hard to differentiate from healthy leaves. This report's goal is to provide documentation for a project that used CNN to detect plant diseases with a 96.84% accuracy rate. The project makes use of a Kaggle dataset that includes pictures of several plant diseases. Creating a Convolutional Neural Network (CNN) model that can correctly classify the photos and identify plant diseases is the goal.
- PROPOSED TREATMENT:
The proposed treatment for the "Plant Disease Detection" project involves the integration of advanced technology, specifically artificial intelligence and machine learning algorithms, to create an automated and accurate system for identifying and diagnosing plant diseases. The solution will utilize a vast dataset of plant images, encompassing various species and their associated diseases. Convolutional Neural Networks (CNNs) will be employed to analyze and learn intricate patterns and features within these images, enabling the model to distinguish between healthy and diseased plants. Additionally, the system will be designed to classify specific diseases, providing detailed insights for targeted treatment strategies. To enhance real�world applicability, the solution will be developed as a user-friendly application, accessible to farmers and agricultural experts. By deploying this comprehensive approach, the proposed treatment aims to revolutionize the current methods of plant disease detection, offering a scalable and efficient solution for mitigating the impact of diseases on crop yield and agricultural sustainability. The model's parameters are optimized during the training phase using stochastic gradient descent and a properly selected learning rate. Over-fitting is avoided by using regularization techniques like dropout and weight decay. The model's efficacy in identifying plant diseases can be evaluated using a variety of factors, including accuracy, precision, recall, and F1-score. To compare the proposed CNN model to current methods for detecting plant diseases, a comparative analysis is done. The model's advantages and disadvantages are examined, with an emphasis on the model's potential for precise disease detection and its applicability for practical applications. The project's results provide a practical and dependable method that advances the study of plant disease detection. The created CNN model exhibits encouraging outcomes and is highly accurate in recognizing plant illnesses. The results open the door for the use of automated disease detection systems in agriculture, enabling early diagnosis and quick response to lessen the effects of plant diseases and improve crop management techniques.
- PLAN OF WORK:
The proposed treatment for the "Plant Disease Detection" project involves the integration of advanced technology, specifically artificial intelligence and machine learning algorithms, to create an automated and accurate system for identifying and diagnosing plant diseases. The solution will utilize a vast dataset of plant images, encompassing various species and their associated diseases. Convolutional Neural Networks (CNNs) will be employed to analyze and learn intricate patterns and features within these images, enabling the model to distinguish between healthy and diseased plants. Additionally, the system will be designed to classify specific diseases, providing detailed insights for targeted treatment strategies. To enhance real�world applicability, the solution will be developed as a user-friendly application, accessible to farmers and agricultural experts. By deploying this comprehensive approach, the proposed treatment aims to revolutionize the current methods of plant disease detection, offering a scalable and efficient solution for mitigating the impact of diseases on crop yield and agricultural sustainability.
REFERENCES
• Import dataset of plant disease from this website https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset