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Image stitching method for crop roots based on an improved SIFT algorithm

XIANG Zi-Wei,WANG Yun-Bo,YAN Xiao-Fei*   

  1. School of Technology, Beijing Forestry University, Beijing 100083, China
  • Received:2025-02-17 Revised:2025-04-25 Accepted:2025-04-25 Published:2025-05-13
  • Supported by:
    This study was supported by the National Natural Science Foundation of China (31971576).

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Abstract: Obtaining accurate information on crop root systems is essential for studying nutrient uptake and water use efficiency. While the widely used minirhizotron method can capture root images in situ, it remains challenging to integrate these localized images into a continuous map of root distribution, thereby limiting the ability to extract and analyze root phenotypic traits comprehensively. To address this limitation, we propose an efficient and rapid root image stitching method based on a root system image acquisition pipeline robot independently developed by our research group. The method consists of four main steps. First, the robotic system automatically captures root images, and image quality is enhanced using Gamma correction and the CLAHE algorithm to improve brightness and local contrast. Next, an improved SIFT algorithm is employed to define the overlapping boundaries, while adaptive thresholding is applied to filter high-response feature points. Simultaneously, PCA-based dimensionality reduction is introduced to lower computational complexity. Finally, multi-band fusion technology is used to achieve seamless image stitching. To evaluate performance, three sets of maize root images at different growth stages were tested, and the improved SIFT algorithm was compared with conventional feature extraction methods (ORB, SURF, and SIFT). Results showed that the average contrast and information entropy of the pre-processed images increased by 19% and 15%, respectively. The improved SIFT algorithm achieved correct matching rate improvements of 91.7%, 35.9%, and 24.3% over ORB, SURF, and SIFT, respectively, and enhanced time efficiency by factors of 1.12, 11.57, and 1.11. Additionally, to assess the robustness and stability of the proposed method, five groups of experiments involving different scaling ratios were conducted. The results demonstrated that the improved SIFT algorithm consistently achieved the highest average overlapping area and percentage. In conclusion, this method can be effectively integrated into automated root image monitoring systems, providing a reliable foundation for subsequent phenotypic analysis of crop root systems.

Key words: image stitching, SIFT algorithm, feature extraction, feature matching, pipeline robot

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