College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
Comput Intell Neurosci. 2022 Apr 15;2022:4848425. doi: 10.1155/2022/4848425. eCollection 2022.
Tomato is an important and fragile crop. During the course of its development, it is frequently contaminated with bacteria or viruses. Tomato leaf diseases may be detected quickly and accurately, resulting in increased productivity and quality. Because of the intricate development environment of tomatoes and their inconspicuous disease spot features and small spot area, present machine vision approaches fail to reliably recognize tomato leaves. As a result, this research proposes a novel paradigm for detecting tomato leaf disease. The INLM (integration nonlocal means) filtering algorithm, for example, decreases the interference of surrounding noise on the features. Then, utilizing ResNeXt50 as the backbone, we create DCCAM-MRNet, a novel tomato image recognition network. Dilated Convolution (DC) was employed in STAGE 1 of the DCCAM-MRNet to extend the network's perceptual area and locate the scattered disease spots on tomato leaves. The coordinate attention (CA) mechanism is then introduced to record cross-channel information and direction- and position-sensitive data, allowing the network to more accurately detect localized tomato disease spots. Finally, we offer a mixed residual connection (MRC) technique that combines residual block (RS-Block) and transformed residual block (TR-Block) (TRS-Block). This strategy can increase the network's accuracy while also reducing its size. The DCCAM-classification MRNet's accuracy is 94.3 percent, which is higher than the existing network, and the number of parameters is 0.11 M lesser than the backbone network ResNeXt50, according to the experimental results. As a result, combining INLM and DCCAM-MRNet to identify tomato diseases is a successful strategy.
番茄是一种重要而脆弱的作物。在其生长过程中，经常会受到细菌或病毒的污染。快速、准确地检测番茄叶病害，可以提高产量和质量。由于番茄复杂的生长环境以及其病斑特征不明显、病斑面积小等特点，现有的机器视觉方法无法可靠地识别番茄叶片。因此，本研究提出了一种新的番茄叶片病害检测方法。例如，INLM（集成非局部均值）滤波算法可以减少周围噪声对特征的干扰。然后，我们利用 ResNeXt50 作为骨干网络，创建了一种新的番茄图像识别网络 DCCAM-MRNet。在 DCCAM-MRNet 的 STAGE1 中使用了 Dilated Convolution（DC）来扩展网络的感知区域，并定位番茄叶片上分散的病斑。然后引入坐标注意力（CA）机制来记录跨通道信息和方向敏感以及位置敏感的数据，使网络能够更准确地检测局部化的番茄病斑。最后，我们提出了一种混合残差连接（MRC）技术，将残差块（RS-Block）和变换残差块（TR-Block）（TRS-Block）相结合。该策略可以提高网络的精度，同时减少网络的参数量。实验结果表明，DCCAM-classification MRNet 的准确率为 94.3%，比现有网络更高，参数量比骨干网络 ResNeXt50 少 0.11M。因此，将 INLM 和 DCCAM-MRNet 结合起来识别番茄病害是一种成功的策略。
