Sun Hao, Fu Rui, Wang Xuewei, Wu Yongtang, Al-Absi Mohammed Abdulhakim, Cheng Zhenqi, Chen Qian, Sun Yumei
Shandong Facility Horticulture Bioengineering Research Center, Weifang University of Science and Technology, Weifang, 262700, China.
Department of Smart Computing, Kyungdong University, 46 4-gil, Bongpo, Giosung, 24764, Gangwon-do, Korea.
BMC Plant Biol. 2025 Mar 11;25(1):311. doi: 10.1186/s12870-025-06247-w.
In the context of intelligent agriculture, tomato cultivation involves complex environments, where leaf occlusion and small disease areas significantly impede the performance of tomato leaf disease detection models. To address these challenges, this study proposes an efficient Tomato Disease Detection Network (E-TomatoDet), which enhances tomato leaf disease detection effectiveness by integrating and amplifying global and local feature perception capabilities. First, CSWinTransformer (CSWinT) is integrated into the backbone of the detection network, substantially improving tomato leaf diseases' global feature-capturing capacity. Second, a Comprehensive Multi-Kernel Module (CMKM) is designed to effectively incorporate large, medium, and small local capturing branches to learn multi-scale local features of tomato leaf diseases. Moreover, the Local Feature Enhance Pyramid (LFEP) neck network is developed based on the CMKM module, which integrates multi-scale features across different detection layers to acquire more comprehensive local features of tomato leaf diseases, thereby significantly improving the detection performance of tomato leaf disease targets at various scales under complex backgrounds. Finally, the proposed model's effectiveness was validated on two datasets. Notably, on the tomato leaf disease dataset, E-TomatoDet improved the mean Average Precision (mAP50) by 4.7% compared to the baseline model, reaching 97.2% and surpassing the advanced real-time detection network YOLOv10s. This research provides an effective solution for efficiently detecting vegetable pests and disease issues.
在智能农业背景下，番茄种植涉及复杂环境，其中叶片遮挡和小面积病害区域严重阻碍了番茄叶部病害检测模型的性能。为应对这些挑战，本研究提出了一种高效的番茄病害检测网络（E-TomatoDet），通过整合和增强全局与局部特征感知能力来提高番茄叶部病害检测的有效性。首先，将CSWinTransformer（CSWinT）集成到检测网络的主干中，大幅提高了番茄叶部病害的全局特征捕捉能力。其次，设计了一个综合多内核模块（CMKM），有效地纳入大、中、小局部捕捉分支，以学习番茄叶部病害的多尺度局部特征。此外，基于CMKM模块开发了局部特征增强金字塔（LFEP）颈部网络，该网络整合了不同检测层的多尺度特征，以获取更全面的番茄叶部病害局部特征，从而显著提高了复杂背景下不同尺度番茄叶部病害目标的检测性能。最后，在两个数据集上验证了所提模型的有效性。值得注意的是，在番茄叶部病害数据集上，E-TomatoDet与基线模型相比，平均精度均值（mAP50）提高了4.7%，达到97.2%，超过了先进的实时检测网络YOLOv10s。本研究为高效检测蔬菜病虫害问题提供了一种有效解决方案。
