Integrating Deep AI with Plant Disease Diagnosis: Toward Early Detection and Sustainable Crop Protection

This paper explores the integration of deep learning—a subset of AI— with plant pathology to revolutionize diagnosis using a MobileNetV2 CNN trained on the Plant Village dataset (31,718 training, 4,514 test images). Our approach achieved 99.4% validation accuracy, highlighting practical potential for early detection and reduced pesticide use, aligning with sustainable agriculture. The study reviews CNNs, GANs, data challenges, and future integration with IoT and drones for smarter disease management. It explores the integration of deep learning—a subset of AI— with plant pathology to revolutionise the diagnosis and treatment of plant diseases, a crucial concern for global food security. By harnessing the capabilities of deep learning algorithms to analyze and interpret complex patterns in image data, researchers and practitioners can identify plant diseases with unprecedented accuracy and speed. This advancement not only facilitates early detection and treatment but also minimizes the reliance on chemical interventions, aligning with sustainable agriculture practices. A thorough examination of contemporary methods, including convolutional neural networks (CNNs) and generative adversarial networks (GANs), this study illustrates the significant strides made in automating disease detection. Furthermore, the paper delves into the challenges and opportunities that lie ahead, such as data scarcity, the need for dataset diversity, and the integration of AI tools into existing agricultural frameworks. By providing a synthesis of current research and potential future directions, this study aims to shed light on the transformative impact of AI on plant pathology and the broader implications for agritech innovation