System-on-a-chip (SoC) FPGA-based Hardware Acceleration for Green Crop Segmentation

Abstract

With the development of artificial intelligence technology, agricultural robot plays a significantly important role for agricultural intelligence. Crop segmentation is a critical and fundamental pre-processing step for agricultural robot navigation and crop growth monitoring. Although there are several crop segmentation methods, they cannot meet the online real-time requirement for agricultural robot. In view of this, a real-time crop segmentation system implemented on a SoC-FPGA is first proposed in this paper, which contains image loading module, segmentation algorithm module and image display module. As a core module, segmentation algorithm module consists of greenness identification, thresholding operation and morphological operation. Its hardware implementation adopted a parallel pipeline architecture to enhance real-time performance. The proposed system is implemented on a Xilinx Zynq-7000 FPGA and the experimental results showed that it can achieve a frame rate of 60 frames per second for the image resolution of 1920×1080 with the average accuracy of 91%.