An FPGA-Based Hardware/Software Design Using Binarized Neural Networks for Agricultural Applications: A Case Study

Abstract

This work presents an FPGA-based hardware/software design to help the agricultural robot intelligently decide if biological agents need to be applied to the target crops. For target crop recognition, in our global positioning, the selective search integrates with a thresholding scheme to reduce the number of region of interest (ROI) in a captured image. In our local recognition, a binarized neural network (BNN) architecture is presented to help recognize the target crop. Furthermore, an estimation method of pest and disease severity is also presented. Experiments show integrating our presented BNN architecture needs a few extra resources (less than 17% of available FPGA resources in terms of Xilinx Zynq UltraScall+ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">™</sup> MPSoC ZU3EG A484), compared to an existing BNN one. However, the top-1 accuracy rate and the top-5 one can be increased by 32.25% to 32.84% and by 14.99% to 15.17%, respectively. Furthermore, when the presented BNN architecture was also implemented on the ARM Cortex-A53 CPU and the NVIDIA GeForce RTX 2080 GPU, our BNN hardware module on the FPGA can accelerate the frames per second (FPS) by a factor of 3,690.18 and a factor of 1.07, respectively.