An AIoT-Based System for Efficient Classification of Rice Leaf Diseases†

Abstract

The quantity and quality of rice output are significantly impacted by rice diseases. The accurate and fast diagnosis of rice diseases is the key to avoiding these negative effects. Recent remarkable advancements in the fields of the Internet of Things (IoT) and artificial intelligence (AI) have given rise to a novel concept known as Artificial Intelligence of Things (AIoT). In this paper, an AIoT-based system has been designed for the efficient classification of rice leaf diseases. Machine learning models have been built based on deep convolutional neural networks such as YOLO and ResNet and evaluate the capability of distinguishing healthy cases from three types of rice leaf diseases, including leaf blast, hispa, and brown spot, and healthy. Each machine learning model provides quite good classification results; however, there are still some confusion cases between types of diseases. The most optimal machine learning model was used to deploy on IoT platforms, including servers and web applications. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.