Plant Disease detection using Machine learning and CNN on leaf images

This paper proposes a system for detection of the diseases in plants using machine learning and image processing technique to analyze the images of leaves and fruits. Leveraging convolutional neural networks (CNNs) and various machine learning approaches, our system aims to accurately detect and classify plant diseases for improved agricultural management. We draw inspiration from previous studies, incorporating methodologies such as data augmentation, feature extraction, and outlier identification to enhance detection accuracy and efficiency. The proposed system is evaluated using diverse datasets, encompassing a range of plant species and disease types, to ensure robustness and generalization. Through experimentation, we demonstrate the efficiency of CNNs and algorithms of machine learning in detecting diseases and classifying plant species, highlighting their potential for real-world applications in agriculture. Additionally, we focused on the importance of selection of dataset, evaluation metrics, and optimization of model for achieving reliable disease detection results.