Advancing the Future of Agricultural Sustainability: Utilizing Deep Learning for Plant Disease Detection

In the complex agricultural environment, precisely detecting plant diseases is the first and most important step in preventing them. This identification process has changed drastically, moving in the direction of digitalization and data-driven techniques, due to the rapid advancement of technology in farming. As a result of this development, smart farming can incorporate complex analytical tools, strategic planning, and decision-making support systems. A major step forward in contemporary farming is the digitization and data-driven revolution in plant disease detection. Sustainable agriculture in the face of complicated environmental difficulties is possible when farmers use technology and data to their advantage in the fight against diseases. This will increase crop yields while decreasing losses. The present research study presents an innovative framework designed to improve the detection and recognition of plant diseases by utilizing deep learning techniques. This framework is specifically created to enhance the precision, comprehensiveness, and effectiveness of diagnosing plant diseases in agricultural environments. At first, the framework utilises a region proposal network (RPN) to accurately identify and locate leaves in challenging environmental conditions. This phase is essential for separating the pertinent sections of the image for further investigation. After localizing the leaves, the images are inputted into a classification system, such as a finetuned convolutional neural network (CNN) known as MobileNet-V2, to identify and recognize diseases. The execution of this framework yielded highly accurate findings, with an impressive accuracy rate of 97.97%. The level of precision exceeds that of conventional techniques, demonstrating a substantial enhancement in illness detection capability. The suggested framework effectively reduces the negative effects of plant diseases on agricultural production, leading to sustainable development in agriculture. The implementation of deep learning algorithms, as suggested in this study paper, has significant implications in diverse fields such as intelligent agriculture, ecological conservation, and agricultural efficiency.