Detecting Plant Disease Using Machine Learning

Plant diseases are becoming more common, which is a serious danger to the world's food security. Manual examination is a common component of traditional illness detection procedures, which can cause delays in diagnosis and treatment. Convolutional Neural Networks (CNNs), one type of deep learning technology, have demonstrated encouraging results in automating the detection and categorization of plant diseases in recent years. This work uses high-resolution plant leaf photos to investigate the use of CNNs for plant disease detection. The essential components of the suggested system are picture capture, preprocessing, feature extraction, CNN model training, and illness classification. Model assessment and training are conducted using an extensive dataset that includes tagged photos of both healthy and ill plants. The study assess how well the CNN-based plant disease detection system performs. The efficacy of the CNN-based plant disease detection system is assessed in the study by means of measures including F1-score, recall, accuracy, and precision. The model is also contrasted with conventional techniques to demonstrate its superiority in terms of accuracy and speed. The outcomes show how well the suggested CNN method works to identify different plant illnesses, opening the door for the creation of a trustworthy and effective automated plant disease detection system.