发表机构
Indian Institute of Technology Ropar; Plaksha University（印度理工学院罗帕尔分校; 普拉卡莎大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用卫星图像时间序列检测作物胁迫，提出基于自监督3D卷积自动编码器的STS-NET，利用四个植被指数捕捉胁迫模式，在实际甘蔗数据集上评估，结果显示其能有效检测胁迫且依赖标签数据少，还可作特征提取器。
AI 中文摘要
早期准确检测作物胁迫对提高农业生产力和确保全球粮食安全至关重要，但收集大量带标签的作物胁迫数据集具有挑战性。为此，我们引入了基于自监督3D卷积自动编码器构建的新型时空胁迫网络（STS-NET），利用卫星图像时间序列数据进行作物胁迫检测。它利用从高分辨率Planetscope图像获得的四个植被指数捕捉时空胁迫模式。该模型在BSPT数据集上训练，并在印度北方邦拉克希姆布尔-凯里地区一个2.5英亩试验田一年收集的实际甘蔗数据集上评估。结果表明STS-NET能有效检测甘蔗作物胁迫，对标签数据依赖小，还可作为简单模型的强大特征提取器。
英文摘要
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.
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