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This paper introduces STS-NET, a self-supervised spatio-temporal stress detection model that leverages Satellite Image Time Series (SITS) data to identify crop stress without requiring extensive labeled datasets. By employing a 3D-convolutional autoencoder and utilizing four vegetation indices derived from high-resolution imagery, STS-NET demonstrates impressive precision rates of 97.98% for water stress, 85.08% for nitrogen stress, and 83.47% for combined stress in sugarcane crops. These findings highlight STS-NET's effectiveness in enhancing agricultural productivity and food security through early stress detection.
STS-NET achieves near-perfect precision in detecting crop water stress while minimizing the need for labeled training data, revolutionizing agricultural monitoring.
Early and accurate detection of crop stress is essential to improve agricultural productivity and ensure global food security. However, collecting a large labeled crop stress dataset is a challenging task. To address this challenge, we introduce a novel spatial-temporal stress network (STS-NET), built on a self-supervised 3D-convolutional autoencoder (3D-CAE), designed to utilize Satellite Image Time Series (SITS) data for crop stress detection. STS-NET exploits four vegetation indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index (GNDVI), Red-Edge Chlorophyll Index (RECI) and Normalized Difference Red-Edge Index (NDRE) obtained from high resolution Planetscope imagery to capture spatiotemporal stress patterns. The model is trained on our BSPT (Barnala Spatial-Temporal) dataset and evaluated on a real-world sugarcane dataset collected over a year from a 2.5-acre test plot located in Lakhimpur-Kheri (LK) district in Uttar Pradesh in India. STS-NET achieved a precision of 97. 98\% for water stress, 85.08\% for nitrogen stress, and 83.47\% for combined stress. The results demonstrate the potential of STS-NET in effectively detecting stress in sugarcane crops with minimal reliance on labeled data. Furthermore, STS-NET can serve as a robust feature extractor for simpler models.
