Tang Zhiwen, He Xinyu, Zhou Guoxiong, Chen Aibin, Wang Yanfeng, Li Liujun, Hu Yahui
College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, Hunan, China.
College of Bangor, Central South University of Forestry and Technology, Changsha 410004, Hunan, China.
Plant Phenomics. 2023 May 12;5:0042. doi: 10.34133/plantphenomics.0042. eCollection 2023.
Tomato leaf diseases have a significant impact on tomato cultivation modernization. Object detection is an important technique for disease prevention since it may collect reliable disease information. Tomato leaf diseases occur in a variety of environments, which can lead to intraclass variability and interclass similarity in the disease. Tomato plants are commonly planted in soil. When a disease occurs near the leaf's edge, the soil backdrop in the image tends to interfere with the infected region. These problems can make tomato detection challenging. In this paper, we propose a precise image-based tomato leaf disease detection approach using PLPNet. First, a perceptual adaptive convolution module is proposed. It can effectively extract the disease's defining characteristics. Second, a location reinforcement attention mechanism is proposed at the neck of the network. It suppresses the interference of the soil backdrop and prevents extraneous information from accessing the network's feature fusion phase. Then, a proximity feature aggregation network with switchable atrous convolution and deconvolution is proposed by combining the mechanisms of secondary observation and feature consistency. The network solves the problem of disease interclass similarities. Finally, the experimental results show that PLPNet achieved 94.5% mean average precision with 50% thresholds (mAP50), 54.4% average recall (AR), and 25.45 frames per second (FPS) on a self-built dataset. The model is more accurate and specific for the detection of tomato leaf diseases than other popular detectors. Our proposed method may effectively improve conventional tomato leaf disease detection and provide modern tomato cultivation management with reference experience.
番茄叶部病害对番茄种植现代化有重大影响。目标检测是病害预防的一项重要技术，因为它可以收集可靠的病害信息。番茄叶部病害在多种环境中发生，这会导致病害的类内变异性和类间相似性。番茄植株通常种植在土壤中。当病害出现在叶片边缘附近时，图像中的土壤背景往往会干扰感染区域。这些问题会使番茄检测具有挑战性。在本文中，我们提出了一种基于精确图像的番茄叶部病害检测方法，即使用PLPNet。首先，提出了一种感知自适应卷积模块。它可以有效地提取病害的特征。其次，在网络的颈部提出了一种位置增强注意力机制。它抑制了土壤背景的干扰，并防止无关信息进入网络的特征融合阶段。然后，通过结合二次观察和特征一致性机制，提出了一种具有可切换空洞卷积和反卷积的邻近特征聚合网络。该网络解决了病害类间相似性的问题。最后，实验结果表明，PLPNet在自建数据集上，在50%阈值下平均精度均值（mAP50）达到94.5%，平均召回率（AR）为54.4%，每秒帧数（FPS）为25.45。该模型在检测番茄叶部病害方面比其他流行的检测器更准确、更具特异性。我们提出的方法可以有效地改进传统的番茄叶部病害检测，并为现代番茄种植管理提供参考经验。
