Real-Time Crop Stress Monitoring and Early Warning System for Paddy and Maize Using Multi-Temporal Sentinel-2 Data and Deep Learning in Semi-Arid Regions

Abstract

Semi-arid regions with a high potential for rice and maize cultivation have become some of the most actively farmed areas. They now face the challenge of achieving food security despite the threats of crop water stress, nutrient loss, and environmental changes. In this paper, we develop a real-time crop stress monitoring and early warning system that utilizes multi-temporal Sentinel-2 images and deep learning models in Mahabubabad district, Telangana, India. Different types of crop stresses such as water stress, nutrient deficiency, and phenological anomalies were detected and classified using a hybrid CNN-LSTM architecture with an attention mechanism. The methodology was based on 874 field polygons with extensive in-situ data collection during 2023-24, incorporating multi-temporal spectral indices (NDVI, EVI, NDWI, REP), weather variables, and soil characteristics. The total classification accuracy reached 89.4% for paddy and 87.2% for maize over all stress types, showing that stress detection from satellite images is quite reliable. Water stress was the category that was detected most accurately (92.1% for paddy and 89.8% for maize), followed by nutrient stress (88.7% and 86.3%) and phenological stress (85.2% and 83.9%). The warning system made it possible to identify the problem 15-25 days before there were visible symptoms, making it possible for the farm management to respond in time. Activities of the farm that were most vulnerable to detection were air and water temperatures, precipitation, and crop growth stages for water stress 45-60 days after sowing, 30-45 days for nutrient stress, and during the reproductive phase for phenological stress. The system could be extended for industrial crop stress monitoring across the semi-arid agricultural systems which might lead to precision agriculture and climate-resilient farming practices.

Plant phenotyping relevance

衛星画像と深層学習を用いて作物の水ストレス・栄養ストレス・生育異常を直接推定し、精度検証と早期検出性能を評価しているため、植物表現型取得法が中心である。

Code and data availability

The article describes a CNN-LSTM crop stress monitoring system using Sentinel-2 data and 874 field polygons, but contains no data availability statement, no public repository deposit, no author code/model release, and no supplement reference. All URLs in the text are citations to prior work, not paper-specific assets.

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