STS-NET: Spatio-Temporal Stress Network for Self-Supervised Crop Stress Detection using Satellite Image Time Series(基于卫星时序影像的自监督作物胁迫时空网络)
基于卫星时序影像的自监督作物胁迫检测方法，具有创新性和实用价值，但发表于两周前，时效性较低。
及时准确地检测作物胁迫对提升农业生产力和保障全球粮食安全至关重要,但标注数据成本高。文章提出新型时空应力网络STS-NET,基于自监督3D卷积自编码器构建,利用PlanetScope高分辨率影像时序数据,提取NDVI、GNDVI、RECI、NDRE四种植被指数捕捉时空胁迫模式。在印度北方邦Lakhimpur-Kheri地区2.5英亩甘蔗试验田上,STS-NET对水分胁迫达到97.98%精度、对氮素胁迫达85.08%精度、对复合胁迫达83.47%精度,证明该自监督方法在标注数据稀缺条件下有效检测甘蔗胁迫的潜力,并可作为简单模型的稳健特征提取器。[预印本]
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.
Comments:5 pages
Subjects:Computer Vision and Pattern Recognition (cs.CV)
Cite as:arXiv:2607.18791 [cs.CV]
(or arXiv:2607.18791v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.18791
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Rajiv Ranjan [view email]
[v1] Tue, 21 Jul 2026 07:11:03 UTC (2,480 KB)