Real-time detection of crop rows in maize fields based on autonomous extraction of ROI

Abstract

The current crop rows detection based on machine vision generally has the problems of low detection accuracy and poor real-time performance. Moreover, crop rows detection remains a challenging problem in complex field conditions, such as high weeds pressure, poor illumination conditions, and vegetation foliage shading. We propose a crop rows detection algorithm based on autonomous extraction of ROI (Region of interest). The prior method computes the feature points of the entire image and groups them into the crop rows which they belong to. Instead, we consider the core of crop rows detection problem to be the extraction of the travelling area of agricultural machinery in maize fields. A YOLO (You Only Look Once) neural network is employed to predict the travelling area of the agricultural machinery end-to-end. The prediction boxes are unified into ROI and the crop and soil background are segmented in the ROI by Excess Green operator and Otsu’s method. Then, the feature points of crops are extracted using FAST (Features from Accelerated Segment Test) corner point detection, and finally the detection lines of crop rows are fitted with least squares method. Because image recognition is limited to a valid region after the ROI is extracted, the processing speed of our algorithm is remarkably fast. It takes only about 25 ms to process a single image (640*360 pixels) and the frame rate of video stream exceeds 40FPS. Meanwhile, it can achieve high accuracy and robust extraction of ROI in various maize fields. The average error angle of the detection lines is 1.88°, which can meet the real-time and accuracy requirements of field navigation. The proposed algorithm can provide a new solution to the current machine vision-based navigation technology for agricultural machinery. Code is available at: https://github.com/WoodratTradeCo/crop-rows-detection.