Disease Detection of Plant Leaf Using Image Processing

Plant disease detection is currently approached from a different angle in this research, which advocates for a model built in Keras with a TensorFlow backend that uses transfer learning. This paper aims to develop an efficient system for the timely detection and classification of plant leaf diseases caused by bacterial, viral, or fungal infections. The suggested system makes use of convolutional neural networks (CNNs), a type of deep learning technique. Accurate leaf disease classification is achieved through the use of CNNs and transfer learning. To validate and train the CNN, a dataset of plant leaves with both normal and infected specimens is used. The trained model is expected to classify plant leaf diseases with an accuracy of 96.20%, demonstrating the efficacy of transfer learning in CNN-based image classification tasks. The proposed system gives farmers the ability to identify plant leaf diseases early on, which is useful in practical situations and allows for prompt intervention. Furthermore, a web application is created with the FLASK API that lets users submit an image of a sample leaf and get back information about the health of the plant.