Time-Aware Late Fusion for Multimodal Rice Growth-Rate Prediction from UAV Imagery and Weather Context
论文提出时间感知多模态融合方法预测水稻生长速率，结合无人机影像与天气数据，方法新颖且数据规模可观，对精准农业有实质贡献。
本文针对精准农业中纯图像模型难以表征季节性上下文的问题,提出时序感知晚期融合(TALF)模型,通过CNN分支编码图像特征、MLP编码上下文特征,仅在回归头合并两条流。相对湿度、风速、累积时间作为上下文输入,在多季节公开水稻苗数据集上,MAE降至0.031(图像单一基线0.1455,早期融合0.0890),容差精准度达94.9%。结果表明,天气与时间上下文能显著提升无人机图像的水稻生长速率预测,图像与表格编码器分离至最后层的晚期融合设计具有竞争力。原文标题:Time-Aware Late Fusion for Multimodal Rice Growth-Rate Prediction from UAV Imagery and Weather Context,作者:Elhadi A O、Darwish S M、Mahdi M A(埃及Zagazig大学/Alexandria大学),发表于MDPI Computers 2026年第15卷第8期523。
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Open Access Article
Time-Aware Late Fusion for Multimodal Rice Growth-Rate Prediction from UAV Imagery and Weather Context
by
Alaa O. Elhadi
Alaa O. Elhadi
SciProfilesScilitPreprints.orgGoogle Scholar
Saad M. Darwish
Saad M. Darwish
SciProfilesScilitPreprints.orgGoogle Scholar
Mahmoud A. Mahdi
Mahmoud A. Mahdi
SciProfilesScilitPreprints.orgGoogle Scholar
1
Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
2
Institute of Graduate Studies and Research, Alexandria University, Alexandria 21568, Egypt
Author to whom correspondence should be addressed.
Computers2026, 15(8), 523; https://doi.org/10.3390/computers15080523
Submission received: 13 June 2026 / Revised: 17 July 2026 / Accepted: 6 August 2026 / Published: 12 August 2026
(This article belongs to the Special Issue Intelligent Computing and Sensing Systems for Sustainable Precision Agriculture)
Abstract
Accurate crop-growth estimation from unmanned aerial vehicle (UAV) imagery is important for precision agriculture, but image-only models can struggle to represent seasonal context. This study evaluates whether combining UAV imagery with weather variables and elapsed time improves continuous rice growth-rate prediction in a single-site, publicly available rice-seedling dataset spanning multiple growing seasons. A Time-Aware Late Fusion (TALF) model is introduced in which a convolutional branch encodes image features, a multilayer perceptron encodes contextual features, and the two streams are merged only at the regression head. Relative humidity, wind speed, and elapsed time are used as contextual inputs after season-aware preprocessing. Evaluation is reported on a chronological multi-season split using internal ablations rather than external generalization claims. TALF achieved a mean absolute error (MAE) of 0.031, compared with 0.1455 for the optimized image-only baseline and 0.0890 for an early-fusion image-and-weather baseline. A secondary tolerance-based metric reached 94.9% under the reported threshold. The results indicate that weather and elapsed-time context improve prediction on this dataset and that separating image and tabular encoders until the final layers is a competitive multimodal learning design under the reported protocol.
**Keywords:**artificial intelligence; deep learning; computer vision; multimodal learning; late fusion; UAV phenotyping; rice growth prediction; precision agricultureartificial intelligence; deep learning; computer vision; multimodal learning; late fusion; UAV phenotyping; rice growth prediction; precision agriculture
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[](mailto:?&subject=From%20MDPI%3A%20%22Time-Aware%20Late%20Fusion%20for%20Multimodal%20Rice%20Growth-Rate%20Prediction%20from%20UAV%20Imagery%20and%20Weather%20Context%22&body=https://www.mdpi.com/4028776%3A Time-Aware Late Fusion for Multimodal Rice Growth-Rate Prediction from UAV Imagery and Weather Context Abstract%3A Accurate crop-growth estimation from unmanned aerial vehicle (UAV) imagery is important for precision agriculture%2C but image-only models can struggle to represent seasonal context. This study evaluates whether combining UAV imagery with weather variables and elapsed time improves continuous rice growth-rate prediction in a single-site%2C publicly available rice-seedling dataset spanning multiple growing seasons. A Time-Aware Late Fusion (TALF) model is introduced in which a convolutional branch encodes image features%2C a multilayer perceptron encodes contextual features%2C and the two streams are merged only at the regression head. Relative humidity%2C wind speed%2C and elapsed time are used as contextual inputs after season-aware preprocessing. Evaluation is reported on a chronological multi-season split using internal ablations rather than external generalization claims. TALF achie