Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm

Lina Zhang, Ziyi Huang, Zhiyin Yang, Bo Yang, Shengpeng Yu, Shuai Zhao, Xingrui Zhang, Xinying Li, Han Yang, Yixing Lin and Helong Yu ()
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Lina Zhang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Ziyi Huang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Zhiyin Yang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Bo Yang: College of Information Engineering, Changchun University of Finance and Economics, Changchun 130217, China
Shengpeng Yu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Shuai Zhao: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Xingrui Zhang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Xinying Li: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Han Yang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Yixing Lin: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Helong Yu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China

Agriculture, 2025, vol. 15, issue 2, 1-15

Abstract: In response to the structural changes of tomato seedlings, traditional image techniques are difficult to accurately quantify key morphological parameters, such as leaf area, internode length, and mutual occlusion between organs. Therefore, this paper proposes a tomato point cloud stem and leaf segmentation framework based on Elite Strategy-based Improved Red-billed Blue Magpie Optimization (ES-RBMO) Algorithm. The framework uses a four-layer Convolutional Neural Network (CNN) for stem and leaf segmentation by incorporating an improved swarm intelligence algorithm with an accuracy of 0.965. Four key phenotypic parameters of the plant were extracted. The phenotypic parameters of plant height, stem thickness, leaf area and leaf inclination were analyzed by comparing the values extracted by manual measurements with the values extracted by the 3D point cloud technique. The results showed that the coefficients of determination (R 2 ) for these parameters were 0.932, 0.741, 0.938 and 0.935, respectively, indicating high correlation. The root mean square error (RMSE) was 0.511, 0.135, 0.989 and 3.628, reflecting the level of error between the measured and extracted values. The absolute percentage errors (APE) were 1.970, 4.299, 4.365 and 5.531, which further quantified the measurement accuracy. In this study, an efficient and adaptive intelligent optimization framework was constructed, which is capable of optimizing data processing strategies to achieve efficient and accurate processing of tomato point cloud data. This study provides a new technical tool for plant phenotyping and helps to improve the intelligent management in agricultural production.

Keywords: tomato plants; red-billed blue magpie optimization algorithm; elite strategy; point cloud segmentation; convolutional neural network; phenotype extraction analysis (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: 2025
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