Multi-Trait Phenotypic Analysis and Biomass Estimation of Lettuce Cultivars Based on SFM-MVS

Tiezhu Li, Yixue Zhang (), Lian Hu, Yiqiu Zhao, Zongyao Cai, Tingting Yu and Xiaodong Zhang ()
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Tiezhu Li: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Yixue Zhang: Basic Engineering Training Center, Jiangsu University, Zhenjiang 212013, China
Lian Hu: Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510640, China
Yiqiu Zhao: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Zongyao Cai: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Tingting Yu: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Xiaodong Zhang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 15, 1-28

Abstract: To address the problems of traditional methods that rely on destructive sampling, the poor adaptability of fixed equipment, and the susceptibility of single-view angle measurements to occlusions, a non-destructive and portable device for three-dimensional phenotyping and biomass detection in lettuce was developed. Based on the Structure-from-Motion Multi-View Stereo (SFM-MVS) algorithms, a high-precision three-dimensional point cloud model was reconstructed from multi-view RGB image sequences, and 12 phenotypic parameters, such as plant height, crown width, were accurately extracted. Through regression analyses of plant height, crown width, and crown height, and the R 2 values were 0.98, 0.99, and 0.99, respectively, the RMSE values were 2.26 mm, 1.74 mm, and 1.69 mm, respectively. On this basis, four biomass prediction models were developed using Adaptive Boosting (AdaBoost), Support Vector Regression (SVR), Gradient Boosting Decision Tree (GBDT), and Random Forest Regression (RFR). The results indicated that the RFR model based on the projected convex hull area, point cloud convex hull surface area, and projected convex hull perimeter performed the best, with an R 2 of 0.90, an RMSE of 2.63 g, and an RMSEn of 9.53%, indicating that the RFR was able to accurately simulate lettuce biomass. This research achieves three-dimensional reconstruction and accurate biomass prediction of facility lettuce, and provides a portable and lightweight solution for facility crop growth detection.

Keywords: lettuce biomass; 3D reconstruction; phenotyping platform; RFR model; machine learning; precision agriculture (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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