Plant Disease Detection and Classification Using Deep Learning

Abstract

Abstract: Agriculture is the backbone of every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Furthermore, it contributes a significant amount to India’s gross domestic product (GDP). Therefore, protecting and enhancing the agricultural sector helps in the development of India’s economy. In this work, a real-time decision support system integrated with a camera sensor module was designed and developed for the identification of plant disease. This research proposes an intelligent method for plant disease classification using image processing techniques. The proposed method aims to assist farmers and experts in identifying and diagnosing plant diseases efficiently and accurately. The system first obtains images of the plant leaves from different perspectives and then preprocesses the images to enhance the quality and remove noise. The preprocessed images are then subjected to feature extraction using a deep convolutional neural network (CNN) model. The features extracted from the CNN are then fed into a classifier for the classification of plant diseases. The proposed method is evaluated using a dataset of plant images with three different types of diseases. The results obtained show that the proposed method achieves high accuracy in the classification of plant diseases, making it a useful tool for plant disease diagnosis and control. The proposed method can be integrated into a mobile application or web based platform for use by farmers and botanists.