融合遥感、气象与土壤数据，用注意力机制提升作物产量时空预测精度。
Attention-based Multi-modal Deep Learning Model of Spatio-temporal Crop Yield Prediction with Satellite, Soil and Climate Data
- 用CNN提取卫星图像空间特征，结合时间注意力机制动态加权关键生长期。
- 在多个区域验证中达到0.89的R²，显著优于传统模型。
- 适合农业政策制定者和精准农业研究者参考使用。
作物产量预测是保障全球粮食安全和政策决策的关键挑战。传统方法因依赖静态数据，难以捕捉环境变量随时间演变的复杂动态关系。本文提出基于注意力的多模态深度学习框架（ABMMDLF），融合多年卫星影像、高分辨率气象时间序列及初始土壤属性，突破单一数据源限制。模型采用卷积神经网络（CNN）提取空间特征，并引入时间注意力机制，根据图像与视频序列的空间特征自适应调整不同物候期的权重。实验表明，该方法在多个区域实现0.89的R²得分，显著优于基线模型。
原文摘要 · Abstract (English)
Crop yield prediction is one of the most important challenge, which is crucial to world food security and policy-making decisions. The conventional forecasting techniques are limited in their accuracy with reference to the fact that they utilize static data sources that do not reflect the dynamic and intricate relationships that exist between the variables of the environment over time [5,13]. This paper presents Attention-Based Multi-Modal Deep Learning Framework (ABMMDLF), which is suggested to be used in high-accuracy spatio-temporal crop yield prediction. The model we use combines multi-year satellite imagery, high-resolution time-series of meteorological data and initial soil properties as opposed to the traditional models which use only one of the aforementioned factors [12, 21]. The main architecture involves the use of Convolutional Neural Networks (CNN) to extract spatial features and a Temporal Attention Mechanism to adaptively weight important phenological periods targeted by the algorithm to change over time and condition on spatial features of images and video sequences. As can be experimentally seen, the proposed research work provides an R^2 score of 0.89, which is far better than the baseline models do.
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