A Multi-Sensor Fusion Approach for the Assessment of Water Stress in Woody Plants

Abstract

Climate change poses significant threats to forest ecosystems, with drought stress being a major factor affecting tree growth and survival. The accurate and early diagnosis of plant water status is, therefore, critical for advancing climate-smart forestry. However, traditional monitoring approaches often rely on single-sensor data or manual field surveys, limiting their capacity to comprehensively capture the complex physiological and structural dynamics of plants under water deficit. To address this gap, this study developed an indoor multi-sensor phenotyping platform, based on a three-axis mobile truss system, which integrates a hyperspectral camera, a thermal infrared imager, and a LiDAR scanner for coordinated high-throughput data acquisition. We further propose a novel hybrid model, the Whale Optimization Algorithm-based Multi-Kernel Extreme Learning Machine (WOA-MK-ELM), which enhances classification robustness by adaptively fusing hyperspectral and thermal features within a dual Gaussian kernel space. We use Perilla frutescens as a model species, achieving an accuracy of 93.03%, an average precision of 93.11%, an average recall of 94.04%, and an F1-score of 0.94 in water stress degree classification. The results demonstrate that the proposed framework not only achieves high prediction accuracy but also provides a powerful prototype and a robust analytical approach for smart forestry and early warning systems.