PLANT DISEASE RECOGNITION AND TREATMENT USING DEEP LEARNING

ABSTRACT

This project presents a comprehensive methodology for developing a plant disease recognition and treatment system leveraging advanced machine learning and computer vision techniques. A large dataset of plant leaf images spanning 38 classes was curated, preprocessed, and analyzed through exploratory data analysis. Relevant features were engineered to enhance the predictive capabilities of deep learning models, including convolutional neural networks (CNNs) and transfer learning with pre-trained architectures like ResNet. Extensive hyperparameter tuning and cross-validation optimized model performance. The system was implemented using Python and the Django web framework, integrating libraries like TensorFlow, Keras, and OpenCV. Rigorous testing procedures, encompassing unit, integration, and acceptance testing, validated the system's functionality and usability. Comprehensive documentation, including technical specifications and user guides, was developed to facilitate system maintenance and adoption. The proposed solution demonstrates remarkable accuracy in diagnosing plant diseases and providing tailored treatment recommendations, empowering stakeholders in agriculture with an innovative tool for precision crop management and sustainable food production. Future enhancements, such as continuous dataset expansion, integration of environmental factors, mobile applications, and incorporation of advanced computer vision techniques, are recommended to amplify the system's impact and ensure its continued relevance.