Target Soybean Leaf Segmentation Model Based on Leaf Localization and Guided Segmentation

Dong Wang, Zetao Huang, Haipeng Yuan, Yun Liang (), Shuqin Tu and Cunyi Yang
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Dong Wang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Zetao Huang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Haipeng Yuan: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Yun Liang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Shuqin Tu: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Cunyi Yang: College of Agriculture, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2023, vol. 13, issue 9, 1-18

Abstract: The phenotypic characteristics of soybean leaves are of great significance for studying the growth status, physiological traits, and response to the environment of soybeans. The segmentation model for soybean leaves plays a crucial role in morphological analysis. However, current baseline segmentation models are unable to accurately segment leaves in soybean leaf images due to issues like leaf overlap. In this paper, we propose a target leaf segmentation model based on leaf localization and guided segmentation. The segmentation model adopts a two-stage segmentation framework. The first stage involves leaf detection and target leaf localization. Based on the idea that a target leaf is close to the center of the image and has a relatively large area, we propose a target leaf localization algorithm. We also design an experimental scheme to provide optimal localization parameters to ensure precise target leaf localization. The second stage utilizes the target leaf localization information obtained from the first stage to guide the segmentation of the target leaf. To reduce the dependency of the segmentation results on the localization information, we propose a solution called guidance offset strategy to improve segmentation accuracy. We design multiple guided model experiments and select the one with the highest segmentation accuracy. Experimental results demonstrate that the proposed model exhibits strong segmentation capabilities, with the highest average precision (AP) and average recall (AR) reaching 0.976 and 0.981, respectively. We also compare our segmentation results with current baseline segmentation models, and multiple quantitative indicators and qualitative analysis indicate that our segmentation results are better.

Keywords: plant phenotype; soybean leaf; image segmentation; target localization (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: 2023
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