Laser Speckle Imaging-Based Data-Driven Sensor for Comprehensive Monitoring of Drought Stress in Soybean Plants

Identification and timely management of drought stress is a major challenge for global food security and sustainable agriculture. Existing techniques for drought identification are indirect, complex, and time consuming and require sophisticated equipment. Therefore, in this work, we present a novel optical sensor based on laser speckle imaging (LSI) integrated with machine learning (ML) for early identification of drought stress in soybean (Glycine max) plants. The objective of the study was to analyze the effectiveness of the proposed sensor in handling three key aspects of drought stress management: (i) detection of drought stress, (ii) early identification of stress symptoms, and (iii) discrimination of contrasting soybean genotypes with known differences in drought tolerance. Experiments were performed under controlled laboratory conditions, and speckle patterns were captured by the LSI sensor. Statistical feature extraction based on a 2D gray-level co-occurrence matrix (2D-GLCM) was employed, and various ML models were trained. XGBoost achieved the highest accuracy (96.67%), followed by random forest and decision tree (95.56%), for early identification and classification of drought in soybean plants. Moreover, the proposed sensor distinguished the two contrasting genotypes examined in this study (drought-tolerant JS 97-52 and drought-susceptible JS 95-60) with an accuracy of 95.83%, indicating that speckle-derived features are sensitive to their differing drought responses. By combining LSI-based photonics sensing with data-driven modelling, this work introduces an efficient solution for drought stress monitoring under controlled conditions, with potential for precision agriculture following further field validation.