FloraScan: Plant Disease Detection Using Machine Learning and Transfer Learning

Abstract: Agriculture is an important sector that supports the livelihood of many people, especially in developing countries. However, plant diseases can reduce crop productivity and cause losses to farmers. Identifying these diseases at an early stage is important so that proper treatment can be given on time. In this project, we developed FloraScan, a web-based system that detects plant diseases from leaf images using deep learning techniques. The system uses a Convolutional Neural Network (CNN) with transfer learning based on MobileNetV2 to classify diseases in tomato, potato, and bell pepper plants. Users can upload an image of a leaf through the web interface, and the system predicts the disease and also provides basic information such as possible treatments and preventive measures. The model achieved an accuracy of around 97.22%, which shows that deep learning can be useful for early plant disease detection.

Keywords: Plant Disease Detection, Deep Learning, CNN, Transfer Learning, MobileNetV2, Agriculture