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朱文杰,孟鑫,李根,等.水稻病虫害目标检测技术研究进展[J].农业工程,2024,14(6):39-46. DOI: 10.19998/j.cnki.2095-1795.2024.06.007
引用本文: 朱文杰,孟鑫,李根,等.水稻病虫害目标检测技术研究进展[J].农业工程,2024,14(6):39-46. DOI: 10.19998/j.cnki.2095-1795.2024.06.007
ZHU Wenjie,MENG Xin,LI Gen,et al.Research progress on rice diseases and pests object detection technology[J].Agricultural Engineering,2024,14(6):39-46. DOI: 10.19998/j.cnki.2095-1795.2024.06.007
Citation: ZHU Wenjie,MENG Xin,LI Gen,et al.Research progress on rice diseases and pests object detection technology[J].Agricultural Engineering,2024,14(6):39-46. DOI: 10.19998/j.cnki.2095-1795.2024.06.007

水稻病虫害目标检测技术研究进展

Research Progress on Rice Diseases and Pests Object Detection Technology

  • 摘要: 水稻病虫害对水稻的生长和产量具有严重影响,在病虫害初期做到有效识别,及时干预保障水稻生长至关重要,水稻病虫害目标检测技术能够较为准确地自动化识别。随着近些年深度学习的快速发展,目标检测技术也取得了重要进展,如YOLO算法、Faster R-CNN算法等。介绍了水稻病虫害目标检测技术的发展历程和研究进展,分析了近些年提出的改进算法和优点,针对不同类型算法在水稻病虫害目标检测领域中的应用场景和不足,展望了未来发展和研究方向,以促进目标检测技术协助水稻种植研究。

     

    Abstract: Rice diseases and pests have a serious impact on rice growth and yield.Therefore, it is crucial to effectively identify and intervene in initial stages of diseases and pests to ensure rice growth.Object detection technology of rice diseases and pests can achieve relatively accurate automated identification.With rapid development of deep learning in recent years, object detection technology has also made important progress, such as YOLO algorithm, Faster R-CNN algorithm, etc.Development and research progress of rice diseases and pests object detection technology were introduced, improved algorithms and highlights proposed by scholars in recent years were analyzed, application scenarios and shortcomings of different types of algorithms in the field of rice diseases and pests object detection were discussed, and future development and research direction were explored, in order to promote object detection technology to assist rice planting research.

     

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