Detection and Classification of Multiple Plant Diseases Using CNN

Plant diseases can have a significant influence on agricultural productivity, quality, and profitability. Early identification and precise diagnosis of plant diseases are essential for successful disease control, which may reduce the use of pesticides and minimize crop losses. In this study, a machine learning-based approach is developed to identify the diseases of various plants using leaf images. Dataset of leaf images of 14 different crops are collected, including apple, pepper, potato, raspberry, blueberry, cherry, peach, soybean, squash, strawberry, corn, grape, orange, and tomato. The images are preprocessed using techniques such as normalization, resizing, and augmentation, to reduce noise and increase the diversity of the dataset. Convolution neural network (CNN) model is used as the back-end and Flask for the front-end to create a user-friendly web application. An interpretability analysis is conducted to understand the features learned by the model, which can provide insights into the underlying causes of the diseases. The study shows the potential of machine learning approaches for early diagnosis and identification of plant diseases, which can assist farmers in taking prompt action and minimizing crop losses. This approach can be extended to other crops and can be integrated with other technologies, such as drones and sensors, for remote and real-time monitoring of crop health.