Plant Disease Detection System Using Convolutional Neural Networks and TensorFlow Lite

This research presents an intelligent system for the classification of plant diseases using a convolutional neural network (CNN) trained on a large dataset of diseased and healthy plant leaves. The model was developed using Python and deep learning libraries such as TensorFlow and Keras, achieving high accuracy in classifying various plant diseases. The trained model is integrated into a user-friendly web application using Streamlit, enabling real-time predictions from uploaded images. The system provides an accessible interface for farmers, researchers, and agricultural workers to detect plant diseases instantly, thereby aiding in timely intervention and crop management. By leveraging transfer learning and a well-curated dataset, the application ensures reliable results while remaining lightweight and easy to deploy. The project demonstrates a scalable approach to agricultural diagnostics using AI, focusing on automation, accuracy, and usability