Crop disease recognition is essential for effective disease control, preventing outbreaks, and minimizing farmers’ losses. Recently, advanced image processing techniques for crop disease detection, based on deep learning, have gained significant popularity. However, deploying these models in real farms remains challenging. First, these solutions often rely on remote (server)-based processing, which requires transmitting a large volume of crop images. This is often impractical in constrained connectivity environments such as rural fields. Deploying deep learning models directly on end devices is also impractical due to their high computational demands. Alternatively, lightweight models have been developed for resource-constrained edge devices and enable local processing. However, their prediction performance is often limited, particularly when handling hard and complex samples. To address these challenges, 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. 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.
Tao et al. (Fri,) studied this question.