RiceBioS: Identification of Biotic Stress in Rice Crops Using Edge-as-a-Service

Abstract

The identification of biotic stress of rice crops using handheld sensing devices is a challenge, as computationally intensive machine learning models are difficult to be executed in these devices. This challenge is exacerbated in farmers’ fields located in remote regions with limited internet connectivity. Thus, an individualiased plant-specific solution to detect biotic stress due to crop infections is required in farms adopting digital agricultural practices. The existing biotic stress detection solutions are deficient in their ability to make decisions in real-time. It is required to have a system that is capable of making decisions at the edge in handheld devices having limited computational capability. This paper proposes RiceBioS, an AI-based deep learning-enabled handheld device for identifying biotic stress in rice crops using the computational capabilities of handheld devices. RiceBioS adopts Edge-as-a-Service (EaaS) as an approach for classifying rice crop images into two categories – healthy and stressed. The biotic stress condition is further diagnosed into two types of infections, fungal (rice blast) and bacterial (bacterial leaf blight of rice) by pruning the shrunk deep learning classification model and incorporating an automated RoI detection and feature extraction workflow, which makes use of adaptive thresholding and hierarchial masking techniques to perform dimensionality reduction. While RiceBioS demonstrates a test accuracy of 93.25%, it exhibits a negligible tradeoff on a smartphone after deployment. This cutting edge solution helps the farmers make informed decisions based on real-time insights provided by the user-friendly mobile application interface of RiceBioS.