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<p>Modern agriculture faces significant challenges in enhancing crop production efficiency and management. Crop monitoring has emerged as a critical component for achieving precision and intelligent agricultural management. This paper presents a comprehensive review of the latest advancements in deep learning-based object detection techniques applied to crop monitoring. Object detection methods are categorized into single-stage and two-stage approaches, further classified based on feature extraction techniques, namely CNN-based and SSM-based methods. This analysis highlights the significant contributions and limitations of these methods across four primary application domains: pest and disease detection, crop growth monitoring, yield estimation, and weed detection. Statistical data indicates that research in these domains accounts for 84% of the total studies in crop monitoring. Additionally, challenges related to data collection and processing, model selection, and optimization are discussed, along with potential solutions. A summary of publicly available datasets, commonly used evaluation metrics, and performance comparisons of mainstream models in crop monitoring research is also provided. In the future, emphasis is placed on algorithm performance optimization, improved dataset quality, and the development of customized solutions tailored for practical agricultural applications. Addressing these challenges will further advance the modernization and intelligence of agricultural practices.</p>

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A survey on deep learning-based object detection for crop monitoring: pest, yield, weed, and growth applications

  • 期刊论文

  • 发表日期：

    2025年6月9日

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  • Huimin Lu,
  • Bingwang Dong,
  • Bingxue Zhu,
  • Songzhe Ma,
  • Zexing Zhang,
  • Jianzhong Peng,
  • Kaishan Song

摘要

Modern agriculture faces significant challenges in enhancing crop production efficiency and management. Crop monitoring has emerged as a critical component for achieving precision and intelligent agricultural management. This paper presents a comprehensive review of the latest advancements in deep learning-based object detection techniques applied to crop monitoring. Object detection methods are categorized into single-stage and two-stage approaches, further classified based on feature extraction techniques, namely CNN-based and SSM-based methods. This analysis highlights the significant contributions and limitations of these methods across four primary application domains: pest and disease detection, crop growth monitoring, yield estimation, and weed detection. Statistical data indicates that research in these domains accounts for 84% of the total studies in crop monitoring. Additionally, challenges related to data collection and processing, model selection, and optimization are discussed, along with potential solutions. A summary of publicly available datasets, commonly used evaluation metrics, and performance comparisons of mainstream models in crop monitoring research is also provided. In the future, emphasis is placed on algorithm performance optimization, improved dataset quality, and the development of customized solutions tailored for practical agricultural applications. Addressing these challenges will further advance the modernization and intelligence of agricultural practices.