AN AGRICULTURAL INTERNET OF THINGS (A-IOT) BASED INTELLIGENT SYSTEM FOR DISEASE PREDICTION USING TRANSFER LEARNING, A CASE STUDY

Abstract

The agricultural Internet of Things (IoT) has altered agricultural output in unprecedented ways. In addition to raising agricultural productivity, it may also significantly raise product quality, lower labor costs, boost farmer income, and achieve agricultural modernization and intelligence. To predict diseases in the agricultural sector, this article proposed an IoT-based smart system for agriculture. The current state of agricultural IoT is first illustrated, along with a summary of its system architecture.  In order to predict diseases in the agricultural domain and advance agricultural IoT, an intelligent system based on agriculture is being proposed in this study. The MATLAB 2020a tool is used for simulation and results. In the proposed industrial IoT based intelligent system, a transfer learning model is applied for the training and validation of rice leaf disease prediction in agriculture industry 4.0. The result of the proposed industrial IoT based intelligent system achieved 96.95% accuracy, which is better than the state-of-the-art published methods.