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

• Published in: Agriculture (Basel) Vol. 15; no. 2; p. 180
• Main Authors: Zhang, Lina, Huang, Ziyi, Yang, Zhiyin, Yang, Bo, Yu, Shengpeng, Zhao, Shuai, Zhang, Xingrui, Li, Xinying, Yang, Han, Lin, Yixing, Yu, Helong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2025
• Subjects: Accuracy; Agricultural management; Agricultural production; Agriculture; Algorithms; Analysis; Area; Artificial neural networks; Automation; Business metrics; convolutional neural network; Data processing; Deep learning; elite strategy; Geospatial data; Image processing; Image segmentation; Labeling; Leaf area; Leaves; Mathematical optimization; Methods; Neural networks; Occlusion; Optimization; Optimization algorithms; Parameters; phenotype extraction analysis; Phenotypes; Phenotyping; Plant extracts; Plants; Plants (botany); point cloud segmentation; red-billed blue magpie optimization algorithm; Root-mean-square errors; Seedlings; Segmentation; Semantics; Stems; Swarm intelligence; Thickness measurement; Three dimensional models; tomato plants; Tomatoes
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture15020180

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 (R2) 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.