Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR

Shaolin Zhang, Qianglong Ma, Shangkun Cheng, Dong An, Zhenling Yang, Biao Ma and Yang Yang ()
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Shaolin Zhang: School of Engineering, Anhui Agricultural University, Hefei 230036, China
Qianglong Ma: School of Engineering, Anhui Agricultural University, Hefei 230036, China
Shangkun Cheng: School of Engineering, Anhui Agricultural University, Hefei 230036, China
Dong An: School of Engineering, Anhui Agricultural University, Hefei 230036, China
Zhenling Yang: School of Engineering, Anhui Agricultural University, Hefei 230036, China
Biao Ma: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Yang Yang: School of Engineering, Anhui Agricultural University, Hefei 230036, China

Agriculture, 2022, vol. 12, issue 12, 1-16

Abstract: As the basic link of autonomous navigation in agriculture, crop row detection is vital to achieve accurate detection of crop rows for autonomous navigation. Machine vision algorithms are easily affected by factors such as changes in field lighting and weather conditions, and the majority of machine vision algorithms detect early periods of crops, but it is challenging to detect crop rows under high sheltering pressure in the middle and late periods. In this paper, a crop row detection algorithm based on LiDAR is proposed that is aimed at the middle and late crop periods, which has a good effect compared with the conventional machine vision algorithm. The algorithm proposed the following three steps: point cloud preprocessing, feature point extraction, and crop row centerline detection. Firstly, dividing the horizontal strips equally, the improved K-means algorithm and the prior information of the previous horizontal strip are utilized to obtain the candidate points of the current horizontal strip, then the candidate points information is used to filter and extract the feature points in accordance with the corresponding threshold, and finally, the least squares method is used to fit the crop row centerlines. The experimental results show that the algorithm can detect the centerlines of crop rows in the middle and late periods of maize under the high sheltering environment. In the middle period, the average correct extraction rate of maize row centerlines was 95.1%, and the average processing time was 0.181 s; in the late period, the average correct extraction rate of maize row centerlines was 87.3%, and the average processing time was 0.195 s. At the same time, it also demonstrates accuracy and superiority of the algorithm over the machine vision algorithm, which can provide a solid foundation for autonomous navigation in agriculture.

Keywords: LiDAR; crop row detection; K-means; horizontal strips (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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