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Low Illumination Soybean Plant Reconstruction and Trait Perception

Author

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  • Yourui Huang

    (School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
    Institute of Environment-Friendly Materials and Occupational Health, Anhui University of Science and Technology, Wuhu 241003, China)

  • Yuwen Liu

    (Institute of Environment-Friendly Materials and Occupational Health, Anhui University of Science and Technology, Wuhu 241003, China
    School of Artificial Intelligence, Anhui University of Science and Technology, Huainan 232001, China)

  • Tao Han

    (School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
    Institute of Environment-Friendly Materials and Occupational Health, Anhui University of Science and Technology, Wuhu 241003, China)

  • Shanyong Xu

    (School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
    Institute of Environment-Friendly Materials and Occupational Health, Anhui University of Science and Technology, Wuhu 241003, China)

  • Jiahao Fu

    (School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
    Institute of Environment-Friendly Materials and Occupational Health, Anhui University of Science and Technology, Wuhu 241003, China)

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.

Suggested Citation

  • Yourui Huang & Yuwen Liu & Tao Han & Shanyong Xu & Jiahao Fu, 2022. "Low Illumination Soybean Plant Reconstruction and Trait Perception," Agriculture, MDPI, vol. 12(12), pages 1-20, December.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:2067-:d:990750
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    References listed on IDEAS

    as
    1. Wen-Hao Su & Ji Sheng & Qing-Yang Huang, 2022. "Development of a Three-Dimensional Plant Localization Technique for Automatic Differentiation of Soybean from Intra-Row Weeds," Agriculture, MDPI, vol. 12(2), pages 1-16, January.
    2. Qinghe Zhao & Zifang Zhang & Yuchen Huang & Junlong Fang, 2022. "TPE-RBF-SVM Model for Soybean Categories Recognition in Selected Hyperspectral Bands Based on Extreme Gradient Boosting Feature Importance Values," Agriculture, MDPI, vol. 12(9), pages 1-16, September.
    3. Chengda Lin & Fangzheng Hu & Junwen Peng & Jing Wang & Ruifang Zhai, 2022. "Segmentation and Stratification Methods of Field Maize Terrestrial LiDAR Point Cloud," Agriculture, MDPI, vol. 12(9), pages 1-18, September.
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