Abstract
Precision agriculture increasingly relies on data-driven methods to address the challenges of crop stress and drought monitoring under changing climatic conditions. Hence, a deep learning (DL) based innovative framework is required for assessing crop stress dynamics, aimed at improving agricultural productivity and ensuring regional food security, because conventional models often struggle to capture subtle spatial and spectral variations, resulting in limited accuracy and generalizability. To overcome these challenges, an innovative and adaptive DL-based approach has been proposed to utilize multi-temporal Sentinel-2 satellite images collected over Haldharmau village, Gonda District, Uttar Pradesh. The model effectively integrates local spectral and spatial details with broader contextual features, enabling crop classification and stress detection to support precision agriculture. The crop stress and drought maps have been generated with the help of a CNN+ViT model (i.e., A hybrid DL-based classification model) based classified images in conjunction with NDVI and NDWI images to evaluate drought-induced crop variability across 2023, 2024, and 2025. The monthly (January to June) stress maps derived from this process provide valuable insights into temporal patterns of crop exposure. The proposed framework offers quantitative insights into temporal and spatial patterns of crop vulnerability and resilience across semi‐arid agricultural landscapes. Therefore, the proposed framework advances precision agriculture modeling by integrating satellite images, deep learning, and phenological analysis, and is readily transferable to other drought-prone regions with analogous crop systems.
Acknowledgements
The authors are thankful to the Advanced Computing Research Laboratory, Department of Computer Application, Integral University, Lucknow, Uttar Pradesh, India, for providing the necessary support to carry out this work. The MCN number issued by the University is IU/R&D/2026-MCN0004367.
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No external funding was received for this research.
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Dr. Tasneem Ahmed is a Guest Editor of the Collection to which this manuscript has been submitted. To avoid any potential conflict of interest, Dr. Tasneem Ahmed has not been involved in the editorial assessment, peer-review process, or decision-making concerning this manuscript. The remaining author declares no competing interests.
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Yasmeen, G., Ahmed, T. An adaptive deep learning framework for multi-temporal crop and drought stress monitoring in precision agriculture.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-70304-z
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DOI: https://doi.org/10.1038/s41598-026-70304-z
Keywords
- Sentinel-2 image
- Crop stress
- Drought monitoring
- Phenological stage
- Precision agriculture
- Semi-arid region
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