A low power consumption mobile based IoT framework for real-time classification and segmentation for apple disease

Abstract

Untreated diseases in plants not only lead to monetary losses but can have adverse implications when consumed. Disease diagnosis requires early detection and analysis of the disease. Apple horticulture has been a significant agriculture industry around the world and is affected by three most prominent domains of disease in apple namely: Blotch, Scab and Rot. In this paper, we provide a real-time mechanism for simultaneous classification and segmentation of the disease which significantly improves the speed of prediction. We have introduced atrous skip connections with UNet (with ResNet as backbone) furthering the performance. Experimental results on our proposed framework, achieves an accuracy of 94.29% to classify the disease and a dice score of 90.01% for segmentation of the diseased part. We also have developed a mobile application to demonstrate the objectives and to facilitate a user-friendly interface for using the proposed framework.