Low Illumination Soybean Plant Reconstruction and Trait Perception

Yourui Huang, Yuwen Liu (), Tao Han, Shanyong Xu and Jiahao Fu
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Yourui Huang: School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Yuwen Liu: Institute of Environment-Friendly Materials and Occupational Health, Anhui University of Science and Technology, Wuhu 241003, China
Tao Han: School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Shanyong Xu: School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Jiahao Fu: School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

Agriculture, 2022, vol. 12, issue 12, 1-20

Abstract: Agricultural equipment works poorly under low illumination such as nighttime, and there is more noise in soybean plant images collected under light constraints, and the reconstructed soybean plant model cannot fully and accurately represent its growth condition. In this paper, we propose a low-illumination soybean plant reconstruction and trait perception method. Our method is based on low-illumination enhancement, using the image enhancement algorithm EnlightenGAN to adjust soybean plant images in low-illumination environments to improve the performance of the scale-invariant feature transform (SIFT) algorithm for soybean plant feature detection and matching and using the motion recovery structure (SFM) algorithm to generate the sparse point cloud of soybean plants, and the point cloud of the soybean plants is densified by the face slice-based multi-view stereo (PMVS) algorithm. We demonstrate that the reconstructed soybean plants are close to the growth conditions of real soybean plants by image enhancement in challenging low-illumination environments, expanding the application of three-dimensional reconstruction techniques for soybean plant trait perception, and our approach is aimed toward achieving the accurate perception of current crop growth conditions by agricultural equipment under low illumination.

Keywords: three-dimensional reconstruction; image enhancement; feature detection and matching; sparse point cloud; dense point cloud (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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