Wang Xuewei, Liu Jun
Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, China.
Plant Methods. 2024 May 9;20(1):61. doi: 10.1186/s13007-024-01188-1.
Tomatoes possess significant nutritional and economic value. However, frequent diseases can detrimentally impact their quality and yield. Images of tomato diseases captured amidst intricate backgrounds are susceptible to environmental disturbances, presenting challenges in achieving precise detection and identification outcomes. This study focuses on tomato disease images within intricate settings, particularly emphasizing four prevalent diseases (late blight, gray leaf spot, brown rot, and leaf mold), alongside healthy tomatoes. It addresses challenges such as excessive interference, imprecise lesion localization for small targets, and heightened false-positive and false-negative rates in real-world tomato cultivation settings. To address these challenges, we introduce a novel method for tomato disease detection named TomatoDet. Initially, we devise a feature extraction module integrating Swin-DDETR's self-attention mechanism to craft a backbone feature extraction network, enhancing the model's capacity to capture details regarding small target diseases through self-attention. Subsequently, we incorporate the dynamic activation function Meta-ACON within the backbone network to further amplify the network's ability to depict disease-related features. Finally, we propose an enhanced bidirectional weighted feature pyramid network (IBiFPN) for merging multi-scale features and feeding the feature maps extracted by the backbone network into the multi-scale feature fusion module. This enhancement elevates detection accuracy and effectively mitigates false positives and false negatives arising from overlapping and occluded disease targets within intricate backgrounds. Our approach demonstrates remarkable efficacy, achieving a mean Average Precision (mAP) of 92.3% on a curated dataset, marking an 8.7% point improvement over the baseline method. Additionally, it attains a detection speed of 46.6 frames per second (FPS), adeptly meeting the demands of agricultural scenarios.
番茄具有重要的营养和经济价值。然而，频繁发生的病害会对其品质和产量产生不利影响。在复杂背景下拍摄的番茄病害图像容易受到环境干扰，在实现精确检测和识别结果方面面临挑战。本研究聚焦于复杂场景中的番茄病害图像，特别强调四种常见病害（晚疫病、灰叶斑病、褐色腐烂病和叶霉病）以及健康番茄。它解决了诸如干扰过多、小目标病变定位不准确以及实际番茄种植场景中假阳性和假阴性率较高等挑战。为应对这些挑战，我们引入了一种名为TomatoDet的番茄病害检测新方法。首先，我们设计了一个集成Swin-DDETR自注意力机制的特征提取模块，构建一个骨干特征提取网络，通过自注意力增强模型捕捉小目标病害细节的能力。随后，我们在骨干网络中融入动态激活函数Meta-ACON，进一步增强网络描绘病害相关特征的能力。最后，我们提出了一种增强型双向加权特征金字塔网络（IBiFPN），用于融合多尺度特征，并将骨干网络提取的特征图输入到多尺度特征融合模块中。这种改进提高了检测精度，并有效减轻了复杂背景下重叠和遮挡病害目标产生的假阳性和假阴性。我们的方法显示出显著的效果，在一个精心策划的数据集上实现了92.3%的平均精度均值（mAP），比基线方法提高了8.7个百分点。此外，它达到了每秒46.6帧（FPS）的检测速度，能够很好地满足农业场景的需求。
