Revolutionizing Soybean Disease Detection using the Internet of Things with Deep Convolution Networks

Soybean diseases pose a significant obstacle to the increase in worldwide demand for soybean, leading to significant reductions in production. This study introduces an innovative approach that utilizes convolution neural network (CNN) and the internet of things (IoT) to efficiently identify and categorize various types of soybean leaf infections. A deep CNN model has been developed using the AlexNet architecture to effectively classify soybean plant diseases. A collection of 11,493 leaf images of 14 soybean diseases, including healthy plants was acquired from the ground using IoT devices and camera modules. Achieving 99.39% and 97.01% accuracy and F1-score respectively, helps not only in early disease detection, but also brings about a transformative approach to sustainable soybean crop management. This initiative lays the foundation for a strengthened agricultural future, enabling increased crop production and improved economic well-being in the cultivation of soybean, all facilitated by the integration of innovative technologies.