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  4. Vol 7, Issue 1, 2024

PCA-Based Method for Extracting Outer Crop Row Lines

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DOI: 10.23977/jipta.2024.070112 | Downloads: 6 | Views: 169

Author(s)

Qiangqiang Xu 1, Zhongwei Xiong 2

Affiliation(s)

1 School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212000, China
2 School of Electrical Engineering, Henan University of Science and Technology, Luoyang, Henan, 471000, China

Corresponding Author

Qiangqiang Xu

ABSTRACT

China has the largest white radish cultivation area in the world, but its level of mechanized harvesting remains low, relying mainly on manual labor. In complex field environments, accurately extracting crop row lines is key to improving harvesting efficiency and reducing crop damage. Traditional image processing techniques perform poorly under varying lighting conditions, weed interference, and plant occlusion. To address these challenges, this study proposes a method for extracting outer crop row lines in white radish fields based on Principal Component Analysis (PCA). This method processes field images through grayscale conversion, binarization, and morphological filtering, combined with PCA for dimensionality reduction, to extract feature lines between crop rows. These feature lines effectively separate the outermost crop row for accurate extraction. Experiments demonstrate that the method shows strong robustness under different lighting conditions and planting patterns, with an overlap rate (OR) exceeding 81%. This method greatly improves the automation of white radish harvesting and provides a valuable reference for row line extraction in other root crops.

KEYWORDS

Machine vision; Crop row lines; PCA; Excess green

CITE THIS PAPER

Qiangqiang Xu, Zhongwei Xiong, PCA-Based Method for Extracting Outer Crop Row Lines. Journal of Image Processing Theory and Applications (2024) Vol. 7: 101-109. DOI: http://dx.doi.org/10.23977/jipta.2024.070112.

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