Spectral-Based Identification of Nutritional Stress in Tomato Seedlings Using Feature Wavelength Selection and Machine Learning

Tomato seedlings are highly sensitive to nutrient deficiencies, and rapid identification of nitrogen (N), phosphorus (P), and potassium (K) stress is essential for precision fertilization. In this study, a novel hierarchical classification framework integrating visible and near-infrared (Vis-NIR) spectroscopy and machine learning was developed for fine-grained identification of nutrient stress in tomato seedlings. A total of 2814 leaf spectra were collected, and multiple preprocessing methods were systematically evaluated. Feature wavelength selection was conducted using the successive projection algorithm (SPA) and Random Frog to reduce redundancy and enhance model performance. Four machine learning models were implemented within a three-stage classification strategy to identify stress occurrence, nutrient type, and deficiency severity across three gradients (50%, 70%, and 100%). Results indicated that multiplicative scatter correction (MSC) achieved the best preprocessing performance. The MSC-SPA-XGBoost model yielded the highest overall classification accuracy of 92.74% across the complete 10-class stress categorization on an independent test set. Bootstrap analysis further confirmed model robustness, with a 95% confidence interval of [0.9024, 0.9436]. Compared with traditional vegetation indices (which achieved a maximum validation accuracy of only 75.73%), the proposed method showed superior discriminative capability for multi-class nutrient stress. These findings demonstrate that Vis-NIR spectroscopy combined with feature-driven machine learning provides a rapid and reliable approach for precision nutrient management in tomato cultivation.