Wang Jinsheng, Wang Tao, Xu Qin, Gao Lu, Gu Guosong, Jia Liangquan, Yao Chong
School of Information Engineering, Huzhou University, Huzhou, 313000, China.
School of Information Science and Engineering, Jiaxing University, Jiaxing, 314001, China.
Plant Methods. 2025 May 17;21(1):63. doi: 10.1186/s13007-025-01381-w.
Pest infestations in rice crops greatly affect yield and quality, making early detection essential. As most rice pests affect leaves and rhizomes, visual inspection of rice for pests is becoming increasingly important. In precision agriculture, fast and accurate automatic pest identification is essential. To tackle this issue, multiple models utilizing computer vision and deep learning have been applied. Owing to its high efficiency, deep learning is now the favored approach for detecting plant pests. In this regard, the paper introduces an effective rice pest detection framework utilizing the Transformer architecture, designed to capture long-range features. The paper enhances the original model by adding the self-developed RepPConv-block to reduce the problem of information redundancy in feature extraction in the model backbone and to a certain extent reduce the model parameters. The original model's CCFM structure is enhanced by integrating the Gold-YOLO neck, improving its ability to fuse multi-scale features. Additionally, the MPDIoU-based loss function enhances the model's detection performance. Using the self-constructed high-quality rice pest dataset, the model achieves higher identification accuracy while reducing the number of parameters. The proposed RP18-DETR and RP34-DETR models reduce parameters by 16.5% and 25.8%, respectively, compared to the original RT18-DETR and RT34-DETR models. With a threshold of 0.5, the average accuracy calculated is 1.2% higher for RP18-DETR than for RT18-DETR.
水稻作物中的害虫侵扰会极大地影响产量和质量，因此早期检测至关重要。由于大多数水稻害虫会影响叶片和根茎，对水稻进行害虫的目视检查变得越来越重要。在精准农业中，快速准确的自动害虫识别至关重要。为了解决这个问题，已经应用了多种利用计算机视觉和深度学习的模型。由于其高效率，深度学习现在是检测植物害虫的首选方法。在这方面，本文介绍了一种利用Transformer架构的有效水稻害虫检测框架，旨在捕捉远距离特征。本文通过添加自行开发的RepPConv模块来增强原始模型，以减少模型主干中特征提取的信息冗余问题，并在一定程度上减少模型参数。通过集成Gold-YOLO颈部来增强原始模型的CCFM结构，提高其融合多尺度特征的能力。此外，基于MPDIoU的损失函数增强了模型的检测性能。使用自行构建的高质量水稻害虫数据集，该模型在减少参数数量的同时实现了更高的识别准确率。与原始的RT18-DETR和RT34-DETR模型相比，所提出的RP18-DETR和RP34-DETR模型的参数分别减少了16.5%和25.8%。在阈值为0.5的情况下，RP18-DETR计算出的平均准确率比RT18-DETR高1.2%。
