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

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Publicado en:Agriculture vol. 15, no. 15 (2025), p. 1662-1690
Autor principal: Li, Tiezhu
Otros Autores: Zhang Yixue, Hu, Lian, Zhao Yiqiu, Cai Zongyao, Yu, Tingting, Zhang, Xiaodong
Publicado:
MDPI AG
Materias:
Accuracy
Height
Agricultural production
Biomass
Corn
Area
Regression analysis
Crops
Cultivars
Machine learning
Lettuce
Prediction models
Decision trees
Efficiency
Crop growth
Image reconstruction
Support vector machines
Phenotyping
Convexity
Portable equipment
Three dimensional imaging
Algorithms
Plant growth
Light
Morphology
Environmental
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Resumen: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 R2 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 R2 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.
ISSN:2077-0472
DOI:10.3390/agriculture15151662
Fuente:Agriculture Science Database