AI-Driven Solutions for Early Detection of Plant Diseases

Plant diseases have a major negative impact on crop yield and quality, making the agricultural sector concerned. Traditionally, plant disease detection is quite laborious and time consuming. The first introduced system is a deep learning-based plant disease identification system using Convolutional Neural Networks (CNNs). While the idea of artificial intelligence is catching up, a subfield of it known as deep learning is highly effective on tasks such as image recognition and classification. A CNN model is developed and trained in this research on a large annotated dataset of high-resolution plant images from different agricultural environments of healthy and diseased plants. Model architecture selection, data collection, preprocessing, training validation design are some key processes. Plant diseases were accurately detected and classified through fine tuning of the CNN model architecture. The performance was evaluated on standard metrics and good accuracy, sensitivity, specificity, precision, recall and Fl score were achieved. This approach shows the effectiveness in early and precise plant disease detection by results. Using AI driven tool for scalability and robustness, sustainable agricultural practices are supported and crop and food security is increased. The proposed system can help the widespread use of AI technologies in agriculture.