Abstract
Hybrid rice row detection at the pollination stage is critical for the automation of in-field pollination agricultural vehicles. The parental crops of hybrid rice are planted at intervals in seed production fields with narrow inter-row spacing. During the advance of the pollination vehicle, in addition to the centerline of the crop row, information on the crop region boundaries is required to guide the vehicle and prevent it from running over the crop. For complete crop row detection, a novel machine vision-based method was presented to identify each of the individual regions of the crop rows, more than the centerlines, by line-shaped mask scanning combined with the vanishing point of the crop rows. The approach consisted of grayscale transformation, vanishing point detection, crop region identification, boundary position fine-tuning and crop region segmentation. Its region detection performance outperformed the convolutional neural network-based (CNN-based) methods with an intersection over union (IoU) of 0.832, an accuracy of 90.48%, a recall of 86.36%, a precision of 98.96% and an f1-Score of 92.23%. Its centerline extraction ability was compared with Hough Transform-based and SegNet-based methods on the basis of average lateral distance (ALD) between the ground truth line and the detected line. The proposed method resulted in an ALD of 1.943 pixels in a 640*360 resolution image, which was superior to the Hough Transform-based (5.704) and the SegNet-based (3.555) methods.
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The authors gratefully acknowledge project funding provided by the Zhejiang key research and development project in China (Grant No. 2022C02005).
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Li, D., Dong, C., Li, B. et al. Hybrid rice row detection at the pollination stage based on vanishing point and line-scanning method. Precision Agric 24, 921–947 (2023). https://doi.org/10.1007/s11119-022-09980-6
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DOI: https://doi.org/10.1007/s11119-022-09980-6