ML-Based Classifier for Precision Agriculture on Embedded Systems

In precision agriculture, effective pest control helps to reduce yield loss and pesticide application. In this research, the pest to be detected and controlled is the moth lobesia botrana, which mainly attacks the vineyard. We present an automatic pest classifier based on machine learning, considering resource-constrained devices in IoT systems. Transfer learning and an ensemble of compression techniques are used to reduce the size of the classifier with a good trade-off between efficiency, effectiveness, and resource utilization. This procedure allows the achievement of a fully on-chip deployment in two technologies: esp32 and SoC-based FPGA Xilinx PYNQ-Z1 and KRIA.