Multi-variety rice yield prediction from heterogeneous UAV time-series data via a cross-period attention transformer

doi:10.1016/j.jia.2026.10.005

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Multi-variety rice yield prediction from heterogeneous UAV time-series data via a cross-period attention transformer

Xiangqian Feng^1,^2,³, Xiaolong Song⁴, Weiyuan Hong², Ziqiu Li⁵, Hao Jiang², Jiangwen Nie¹, Jun Deng⁶, Qingqing Yang⁷, Ke Liu³, Matthew Tom Harrison³, Guang Chu², Danying Wang²^#, Yunbo Zhang¹^#, Song Chen²^#

¹ College of Agriculture, Yangtze University, Jingzhou 434025, China

² State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Hangzhou 310006, China

³ Tasmanian Institute of Agriculture, University of Tasmania, Launceston 7248, Australia

⁴ School of Computer Science and Engineering, Central South University, Changsha 410017, China

⁵ Academy For Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China

⁶ Key Laboratory of Crop Cultivation and Physiology, Education Department of Guangxi Zhuang Autonomous Region, Guangxi University, Nanning 530004, China

⁷ Jingzhou Agro-meteorology Experimental Station, Jingzhou 434025, China

Highlights

Pioneering yield prediction from multi-year, multi-variety heterogeneous UAV time-series data.

The riceCPAT, an improved cross-period attention Transformer, achieves the best accuracy (R²=74%) with fewer parameters.

Abstract
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摘要

在多品种对比试验中，如何协调长度不一的无人机时序数据，一直是作物产量预测面临的难题。本研究构建了一种创新的Transformer模型——水稻跨时期注意力Transformer（riceCPAT），并利用田间种植的500余个品种的多年时序数据开展模型训练。riceCPAT采用分层处理策略：首先由标准Transformer编码器提取浅层特征，保留完整的时序信息；随后基于物候进行自适应分段，并通过平均池化将不等长的片段压缩为固定维度的向量。riceCPAT以最少的参数量（72 561个）显著降低了RMSE（67.96 g m⁻²）和MAE（53.52 g m⁻²），在降低计算复杂度和参数量的同时，预测精度和计算效率均优于其他方法。在涵盖不同生态点的3个独立试验中，迁移验证的平均R²达67%。事后的模型无关特征分析表明，EVI2和MCARI是产量预测的关键特征，灌浆期是最重要的预测时期。研究认为，riceCPAT有效消除了传统对齐方法固有的信息损失，为多品种产量预测提供了一种高效、可扩展的解决方案。

Abstract

Harmonisation of UAV time-series of heterogenous length has long challenged crop yield prognostics in trials contrasting multiple varieties. Through derivation of an innovative Transformer model, Cross-Period Attention Transformer for Rice (riceCPAT), we invoke time-series data from over 500 varieties grown in the field (EXP1&2). We developed riceCPAT employing a hierarchical processing strategy where a standard Transformer encoder first extracted shallow features, retaining complete time-series information. After adaptive segmentation based on phenology, average pooling was conducted to compress segments of heterogeneous length into fixed-dimension vectors. RiceCPAT significantly reduced RMSE (67.96 g m^-2) and MAE (53.52 g m^-2) with the fewest parameters (72,561), lowering computational complexity and parameter count, outperforming other methods in prognostic accuracy and computational efficiency. Transfer validation across three independent experimental sets (EXP3-5) spanning diverse ecological sites achieved an average R² of 67%. Post-hoc model-agnostic feature analysis revealed that EVI2 and MCARI were key features in yield prediction, with grain filling being the most important prognostic. We contend that riceCPAT effectively eliminates information loss inherent in traditional alignment methods, offering an efficient, scalable solution for multi-variety yield prediction.

Keywords: time series heterogeneous data transformer rice yield prediction

Online: 12 October 2026

Fund:

This study was funded by National Key Research & Development Program of China National Key Technology R&D Program (2023YFD2300503), National Natural Science Foundation of China (32572448), the earmarked fund for China Agriculture Research System (CARS-01), the Agricultural Science and Technology Innovation Program (ASTIP), and the Central Public-Interest Scientific Institution Basal Research Fund, China (No. 10).

About author: Xiangqian Feng, E-mail: Xiangqian.feng@utas.edu.au; #Correspondence Song Chen, E-mail: chensong02@caas.cn; Yunbo Zhang, E-mail: yunbo1022@126.com; Danying Wang, E-mail: wangdanying@caas.cn

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Xiangqian Feng, Xiaolong Song, Weiyuan Hong, Ziqiu Li, Hao Jiang, Jiangwen Nie, Jun Deng, Qingqing Yang, Ke Liu, Matthew Tom Harrison, Guang Chu, Danying Wang, Yunbo Zhang, Song Chen. 2026.

Multi-variety rice yield prediction from heterogeneous UAV time-series data via a cross-period attention transformer . Journal of Integrative Agriculture, Doi:10.1016/j.jia.2026.10.005

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