Published Paper Details:

Agriculture is one of the most critical industries globally, feeding the growing population. However, plant diseases pose a significant threat to crop yield and quality, leading to severe economic losses. Timely detection and accurate diagnosis of plant diseases are essential to mitigate their adverse effects. Traditional manual disease detection is time-consuming and prone to error, necessitating an automated, scalable approach. In this paper, we propose DeepLeaf, an AI-driven system for plant disease recognition based on convolutional neural networks (CNNs). The system leverages deep learning to automatically classify plant diseases from leaf images, providing rapid and reliable diagnoses. We describe the system architecture, dataset preparation, model training, and evaluation results. DeepLeaf demonstrates state-of-theart performance on benchmark datasets and shows significant potential for real-world agricultural applications.