Utilizing Multi-View Morphological, Color–Textural and Multispectral Features for Interpretable Estimation of Lettuce Fresh Weight Using Machine Learning

Accurate and reliable prediction of lettuce fresh weight is essential for optimising protected cultivation management and improving the yield and quality. Multimodal data combined with machine learning models have been widely used for monitoring crop growth. However, existing approaches often fail to capture dynamic physiological changes during crop growth, whereas conventional machine learning models are frequently limited by their black-box nature and thus cannot reveal the intrinsic relationships between features and targets. To address the above issues, this study developed a stationary, multi-sensor integrated data acquisition platform under controlled greenhouse conditions. By fusing multi-view morphological structure, color and texture, and multispectral features, the study constructed interpretable machine learning models for predicting the fresh weight of lettuce. Based on the data collected by the platform, 66 initial features covering morphology, color texture, and vegetation indices were extracted from the data. A two-stage feature-selection strategy combining Pearson correlation screening and variance inflation factor (VIF)-based multicollinearity elimination was used to select nine optimal input variables for the model. To achieve an accurate estimation of the fresh weight of lettuce, the system compared six models: Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosted Decision Tree Regression (GBDT), K-nearest neighbour regression (KNN), XGBoost, and Backpropagation Neural Network (BPNN). The results indicate that the SVR model based on multimodal data fusion performed best, with an R2 of 0.93, an RMSE of 3.23 g, an RMSEn of 5.60%, and an MAE of 2.31 g, demonstrating a significantly higher prediction accuracy than the other models. Furthermore, the SHAP interpretation method was used to reveal the contributions of key features to fresh weight estimation and their interaction mechanisms. This study provides a feasible approach and technical guidance for non-destructive estimation of fresh weight in lettuce under controlled conditions, and offers a preliminary basis for the development of phenotypic monitoring models for protected cultivation.