Towards lightweight deep neural network for smart agriculture on embedded systems

Abstract

Agriculture is the pillar industry for human survival. However, various diseases threaten the health of crops and lead to a decrease in yield. Industry 4.0 is making strides in plant illness prevention and detection, other than supporting farmers to improve plantations’ income. To prevent crop diseases in time, this paper proposes, implements, and evaluates a low-power smart camera. It features a lightweight neural network to verify and monitor the growth status of crops. The proposed tiny model features optimized complexity, to be deployed in milliwatt power microcontrollers, and high accuracy. Experimental results show that our work reaches 99% accuracy on a 4-classes dataset and more than 96% for a 10 classes dataset. The compact model size (139 kB) and low complexity enable ultra-low power consumption (2.63 mW per hour) on the battery-powered Sony Spresense platform, which features a six-core ARM Cortex-M4F.