iCrop+: On-Device AI for Crop Disease Detection with Adaptive Offloading over LoRa

What is it about?

We propose iCrop+, a hybrid end-to-end system that integrates on-device AI for local processing with a powerful deep learning model for remote processing. iCrop+ selectively offloads samples via LoRa communication based on the reliability of local classification, leveraging a combination of category-based and sample-based offloading strategies. To further mitigate LoRa’s data rate limitations, the system preprocesses offloaded samples to extract and adaptively transmit only the most informative image segments, ensuring efficient data transmission without compromising accuracy.

Featured Image

Photo by Waqar Mujahid on Unsplash

Photo by Waqar Mujahid on Unsplash

Why is it important?

iCrop+ can operate independently or be mounted on agricultural robots or drones scouting the crop fields for remote monitoring and decision-making, such as crop disease detection. Extensive experiments on a prototype of iCrop+ demonstrate that iCrop+ outperforms two baseline approaches across multiple performance metrics, showcasing its potential for practical deployment in resource-constrained agricultural environments.