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Yield prediction through UAV-based multispectral imaging and deep learning in rice breeding trials

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  • Zhou, Hongkui
  • Huang, Fudeng
  • Lou, Weidong
  • Gu, Qing
  • Ye, Ziran
  • Hu, Hao
  • Zhang, Xiaobin

Abstract

Predicting crop yields with high precision and timeliness is essential for crop breeding, enabling the optimization of planting strategies and efficients resource allocation while ensuring food security. Current research in this field typically does not address the problem of yield prediction in the diverse context of breeding experiments involving numerous varieties. However, evaluating the performance of prediction models across multiple varieties is vital for further model refining and enhancing model robustness and adaptability.

Suggested Citation

  • Zhou, Hongkui & Huang, Fudeng & Lou, Weidong & Gu, Qing & Ye, Ziran & Hu, Hao & Zhang, Xiaobin, 2025. "Yield prediction through UAV-based multispectral imaging and deep learning in rice breeding trials," Agricultural Systems, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:agisys:v:223:y:2025:i:c:s0308521x24003640
    DOI: 10.1016/j.agsy.2024.104214
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    References listed on IDEAS

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    4. Hao, Shirui & Ryu, Dongryeol & Western, Andrew & Perry, Eileen & Bogena, Heye & Franssen, Harrie Jan Hendricks, 2021. "Performance of a wheat yield prediction model and factors influencing the performance: A review and meta-analysis," Agricultural Systems, Elsevier, vol. 194(C).
    5. Bregaglio, Simone & Ginaldi, Fabrizio & Raparelli, Elisabetta & Fila, Gianni & Bajocco, Sofia, 2023. "Improving crop yield prediction accuracy by embedding phenological heterogeneity into model parameter sets," Agricultural Systems, Elsevier, vol. 209(C).
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