Research on Automatic Alignment for Corn Harvesting Based on Euclidean Clustering and K-Means Clustering

Bin Zhang, Hao Xu, Kunpeng Tian, Jicheng Huang, Fanting Kong, Senlin Mu, Teng Wu, Zhongqiu Mu, Xingsong Wang () and Deqiang Zhou ()
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Bin Zhang: School of Mechanical Engineering, Southeast University, Nanjing 211189, China
Hao Xu: School of Mechanical Engineering, Southeast University, Nanjing 211189, China
Kunpeng Tian: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Jicheng Huang: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Fanting Kong: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Senlin Mu: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Teng Wu: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Zhongqiu Mu: Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Xingsong Wang: School of Mechanical Engineering, Southeast University, Nanjing 211189, China
Deqiang Zhou: School of Intelligent Manufacturing, Jiangnan University, Wuxi 214122, China

Agriculture, 2024, vol. 14, issue 11, 1-15

Abstract: Aiming to meet the growing need for automated harvesting, an automatic alignment method based on Euclidean clustering and K-means clustering is proposed to address issues of driver fatigue and inaccurate driving in manually operated corn harvesters. Initially, the corn field environment is scanned using LiDAR to obtain point cloud data, which are then subjected to pass-through filtering and statistical filtering to remove noise and non-corn contour points. Subsequently, Euclidean clustering and K-means clustering methods are applied to the filtered point cloud data. To validate the impact of Euclidean clustering on subsequent clustering, two separate treatments of the obtained point cloud data were conducted during experimental validation: the first used the K-means clustering algorithm directly, while the second involved performing Euclidean clustering followed by K-means clustering. The results demonstrate that the combined method of Euclidean clustering and K-means clustering achieved a success rate of 81.5%, representing a 26.5% improvement over traditional K-means clustering. Additionally, the Rand index increased by 0.575, while accuracy improved by 57% and recall increased by 61%.

Keywords: corn harvesting; automatic alignment; K-means clustering (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: 2024
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