An IoT Framework for Soil Analysis and Soybean Disease Classification using Deep CN

Abstract

This research presents an innovative framework that integrates the Internet of Things (IoT) and newlinemachine learning to advance precision agriculture, focusing specifically on soybean newlinecultivation. In a country like India, where agriculture is the backbone of the economy, farmers newlineface significant challenges such as inefficient crop management, poor soil fertility monitoring, newlineand delayed disease detection, all of which adversely affect crop yields. This research proposes newlinean IoT-enabled system designed to continuously monitor soil health and predict diseases in newlinesoybean crops early, offering solutions that empower farmers with real-time data for better newlinedecision-making. newlineThe key objectives of the research are threefold: developing an IoT-based framework to newlinemonitor soil parameters and crop health, using machine learning algorithms to forecast soil newlinefertility and provide optimal fertilizer recommendations, and applying deep convolutional newlineneural networks (CNNs) for the early detection and classification of soybean diseases. By newlineachieving these goals, the framework enhances the efficiency of farm management practices newlineand contributes to improving crop productivity and sustainability. newlineThe study builds upon an extensive review of current advancements in precision agriculture newlineand the use of IoT devices for crop monitoring. While IoT technology has shown great potential newlinein agricultural applications, its full integration with traditional farming practices remains a newlinechallenge. Furthermore, detecting crop diseases early remains a critical problem that machine newlinelearning and deep learning models, such as CNNs, are well-positioned to address. This research newlinefills these gaps by proposing a comprehensive system that leverages modern technology to newlinesolve the pressing issues faced by farmers. newline