Plant Health AI Analyzer Using Supervised Learning

Abstract: Because plant diseases significantly affect crop output and food quality, they represent a major threat to global food security. Traditional manual methods for identifying diseases are labor-intensive, time-consuming, and often inaccurate. Convolutional Neural Networks (CNNs) are utilized in this AI-based plant health analyzer to detect and categorize plant leaf illnesses in order to get around these restrictions. The Plant Village dataset, which includes thousands of annotated photos of different crop species and disease types, is used to train the suggested system. For multi-class classification, the CNN model is implemented in PyTorch and optimized with the Adam optimizer with cross-entropy loss. Using a Flask-based web interface, users may enter leaf photographs and receive real-time diagnostic results, including the ailment diagnosis and confidence %. To improve model performance and generalization in a range of environmental conditions, a number of data augmentation techniques are employed, such as image flipping, rotation, and scaling. Experiments demonstrating exceptional disease prediction accuracy highlight the value of deep learning for precision agriculture. This clever technique facilitates early disease detection, allowing researchers and farmers to take prompt preventative action and supporting sustainable smart farming methods..