Early Detection of Eggplant Verticillium Wilt Under Magnetized Water Irrigation Using Multi-Source Data Fusion and Ensemble Learning

Verticillium wilt is a major soil-borne disease affecting eggplant production. Early detection before symptom manifestation can reduce yield losses. Compared with traditional destructive physiological assays, sensor-based technologies enable efficient, nondestructive monitoring. However, magnetized water irrigation may reduce phenotypic separability between healthy and diseased plants, complicating disease detection using single-source data. This study developed a multisource data fusion strategy that integrates multispectral reflectance, chlorophyll fluorescence parameters, and Soil Plant Analysis Development (SPAD) values to construct a weighted ensemble model combining extreme gradient boosting (XGBoost) and support vector machine (SVM). Based on a 28-dimensional multisource feature set collected one to seven days post-inoculation (DPI), the model achieved an overall accuracy of 88.1% and an area under the receiver operating characteristic curve (AUC) of 0.925, with accuracies of 90.1% and 86.0% under ordinary and magnetized water irrigation, respectively. Ablation experiments showed that spectral features alone achieved a recall of only 62.9% under magnetized water irrigation, whereas multisource fusion increased recall to 73.2%. Compared with visual inspection, which requires approximately 10 days for disease confirmation, the model identified diseased plants as early as DPI 3, achieving 91.8% accuracy and an AUC of 0.978. Overall, multisource data fusion improved early Verticillium wilt detection across irrigation regimes. This study demonstrates the novelty of utilizing this fusion strategy to overcome the reduced phenotypic separability induced by magnetized water irrigation, providing a robust and nondestructive diagnostic tool for agricultural disease management.