STS-NET: Spatio-Temporal Stress Network for Self-Supervised Crop Stress Detection using Satellite Image Time Series

2026-07-21

AI Summary

This study addresses the scarcity of labeled data in early crop stress detection by proposing STS-Net, the first self-supervised spatiotemporal network tailored for this task. Built upon a 3D convolutional autoencoder, the method leverages high-resolution PlanetScope time-series imagery along with four vegetation indices—NDVI, GNDVI, RECI, and NDRE—to learn spatiotemporal stress patterns without requiring extensive annotations. Evaluated on real-world sugarcane fields, STS-Net achieves detection accuracies of 97.98%, 85.08%, and 83.47% for water stress, nitrogen stress, and combined stress, respectively, demonstrating both high classification accuracy and strong generalization capability.

Abstract

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.

Problem

• crop stress detection
• self-supervised learning
• satellite image time series
• labeled data scarcity
• agricultural productivity

Innovation

• self-supervised learning
• 3D-convolutional autoencoder
• satellite image time series
• crop stress detection
• spatio-temporal modeling