Abstract
With the development of artificial intelligence technology, agricultural robot plays a significantly important role for agricultural intelligence. Crop row line detection is a critical and fundamental step for agricultural robot navigation. Although there are some crop row lines detection methods, few of them can meet the real-time requirement for agricultural robot under complex fields conditions. In view of this, a real-time crop detection system implemented on a SoC FPGA (System-on-a-Chip Field Programmable Gate Array) is first proposed in this paper, which contains crop segmentation and crop row detection, where we design parallel pipeline architecture to enhance real-time performance by using line buffer and sliding windows technologies. At the same time, the fixed point representation is used to reduce the memory resource in this system. The proposed system is evaluated and implemented on Xilinx Zynq UltraScale+ MPSoC ZCU102 SoC-FPGA. The experimental results show that the proposed system can process an image with 1920×1080 resolution only within 210 ms with the average accuracy of 89.7%, which satisfies the real-time requirements of the crop rows recognition.
Highlights
IntroductionWith the developments of electronics and computer technology, autonomous implementation of various agricultural tasks has become possible under field conditions [1]
Traditional agriculture needs more manpower and material resources
Aiming at the above problems, in this paper, we propose a method for automatically detecting the crop rows lines in field image, where the parallel and pipeline architecture is designed and implemented on the SoC FPGA in order to meet the real-time navigation requirements of agriculture robot
Summary
With the developments of electronics and computer technology, autonomous implementation of various agricultural tasks has become possible under field conditions [1]. The price of high-resolution camera falls, making the machine vision technologies be used widely in various field operations, including agricultural robot guidance in relatively complex field environments. The autonomous agricultural robot based on machine vision guidance can improve efficiency and quality of agricultural production, and has become one of the hot topics in agricultural engineering field [2]. The field environment is complicated against crop row recognition; precise control of robot navigating in real-time still has been a bottleneck problem. Existing crop row recognition methods are caught in a dilemma between time consumption
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