Approximating CNN Computation for Plant Disease Detection
This paper presents a plant disease detection and classification method using YOLOv3 (You Only Look Once) model to design an Internet-of-Things (IoT) device that achieves an average of 96.92% of classification accuracy while detecting plant disease for three different classes.
Abstract
Enabling smart technologies in agriculture has led to the improvement of crop productivity. In India, agriculture is a primary occupation, and 70% of the population is dependent on it. Plant diseases cause significant losses in an agriculture-oriented economy. Timely monitoring of plant health and detecting plant disease is a laborious process. Automated monitoring and detection techniques hold great promise for identifying plant condition and providing useful information to facilitate effective agricultural management measures. Deep learning (DL) algorithms improve the detection accuracy in many computer vision applications of smart and precision agriculture. This paper presents a plant disease detection and classification method using YOLOv3 (You Only Look Once) model to design an Internet-of-Things (IoT) device. An approximate computing technique has been adopted that minimizes the computational complexity of DL algorithms to deploy on any embedded devices efficiently. The proposed model achieves an average of 96.92% of classification accuracy while detecting plant disease for three different classes.