Detection of Plant Pathogens-Prediction Using Machine Learning

Plant-related illness present a significant risk to global food security, resulting in substantial crop losses and economic consequences. The timely and accurate detection of these diseases is crucial for effective control and mitigation. Recently, Convolutional Neural Networks (CNNs) have shown impressive success in recognizing images, making them a promising tool for identifying plant diseases based on visual symptoms. This research introduces a novel CNN algorithm specifically designed for the identification of ailments in plants. The proposed CNN architecture is designed to best capture plant disease patterns and variations from pictures, thereby enhancing the accuracy and robustness of disease classification. Extensive datasets encompassing various plant species and disease symptoms are collected and pre-processed to standardize image quality and enhance the model's ability to generalize. The training process involves optimizing the CNN weights using a stochastic gradient descent algorithm on the pre-processed dataset. The hyperparameters of the model are adjusted to minimize over fitting and enhance its performance on an independent validation set. The trained CNN is rigorously evaluated on an independent test dataset to measure precision, recall, F1 score, and construct a confusion matrix.