Evolution, application efficiency, and future prospects of YOLO series models in wheat ear detection

doi:10.14088/j.cnki.issn0439-8114.2025.12.037

Abstract

Abstract: By reviewing YOLO series models and their application research in wheat ear monitoring at home and abroad, it was found that these models, with their excellent real-time performance and high precision advantages, had become a research hotspot in the field of agricultural intelligent perception.Through continuous model optimization and improvement, the accuracy and efficiency of wheat ear detection were continuously improved, providing strong support for the intelligent prediction of wheat yield and the development of agricultural modernization. Although YOLO series models had made significant progress in wheat ear monitoring, there was still room for improvement in lightweight deployment, multi-modal data fusion, and cross-scene generalization ability. Future research should focus on the above directions to further enhance the practicality and robustness of the models, providing more efficient and reliable technical support for smart agriculture.

Key words: YOLO series models, wheat ear, object detection, feature extraction, detection accuracy, evolution rules, application efficiency, future prospects

CLC Number:

WANG Qing, WANG Zhi-qiang, LIU Hong, LIANG Min, ZHANG Yu-chen, LIN Yu. Evolution, application efficiency, and future prospects of YOLO series models in wheat ear detection[J]. HUBEI AGRICULTURAL SCIENCES, 2025, 64(12): 218-227.

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