Work

Drought-Spec-Net: Early Tomato Drought Detection and Potential Yield-Impact Assessment Using Vis–NIR Data

Year: 2026

Type: preprint

Abstract: ABSTRACT Drought stress significantly reduces tomato ( Solanum lycopersicum L.) productivity, and early detection is critical to minimize yield losses through timely interventions. In this study, we developed Drought-Spec-Net, a hybrid 1D convolutional neural network that integrates local and global spectral feature extraction to detect early drought stress from visible and near-infrared (Vis–NIR) spectra data of tomato seedlings. The model was trained on 378 samples using an 80:20 train–test split, with 20% of the training set reserved for validation. Drought-Spec-Net outperformed the evaluated baseline and state-of-the-art models, achieving 97% accuracy, 95% precision, 98% recall, and an F1-score of 97%. To improve the agronomic interpretation of the model outputs, predicted drought probabilities were converted into a literature-informed potential yield-impact indicator using a maximum impact level of 60%. On the test set (76 samples), mapped potential yield-impact values ranged from 0% to 60%, with an average reduction of 12.97%. We also conducted an initial experiment using our greenhouse RGB dataset, collected daily from drought-treated and well-watered tomato plants at West Virginia State University (WVSU). From this dataset, 44 images were selected for ilastik-based canopy segmentation, producing plant-level drought severity indices (DSI) with a mean of 0.28, median of 0.14, and range of 0.01–0.91. Additionally, we trained and fine-tuned a large language model (LLM) based on PLLaMA-7B-Instruct, called AgriLLaMA, for automated agronomic report generation from Drought-Spec-Net outputs. The generated reports summarize predicted stress levels, mapped potential yield impacts, and preliminary management considerations. This integrated approach not only improves early drought stress detection but also delivers quantitative and interpretable estimates of potential productivity losses, providing a complete framework connecting physiological stress detection to actionable agricultural outcomes.

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.64898/2026.09.24.754113