Department of Communication Engineering Chongqing College of Electronic Engineering, Chongqing 401331, China.
Dabashan Branch of Chongqing Academy of Chinese Materia Medica, Chongqing 400065, China.
Comput Intell Neurosci. 2022 May 27;2022:7012399. doi: 10.1155/2022/7012399. eCollection 2022.
The traditional identification methods of Citrus aurantium diseases and pests are prone to convergence during the running process, resulting in low accuracy of identification. To this end, this study reviews the newest methods for the identification of Citrus aurantium diseases and pests based on a deep convolutional neural network (DCNN). The initial images of Citrus aurantium leaves are collected by hardware equipment and then preprocessed using the techniques of cropping, enhancement, and morphological transformation. By using the neural network to divide the disease spots of Citrus aurantium images, accurate recognition results are obtained through feature matching. The comparative experimental results show that, compared with the traditional recognition method, the recognition rate of the proposed method has increased by about 11.9%, indicating its better performance. The proposed method can overcome the interference of the external environment to a certain extent and can provide reference data for the prevention and control of Citrus aurantium diseases and pests.
传统的柑桔病虫害识别方法在运行过程中容易出现收敛，导致识别精度低。为此，本研究基于深度卷积神经网络（DCNN）综述了柑桔病虫害识别的最新方法。通过硬件设备采集柑桔叶片的初始图像，然后采用裁剪、增强和形态变换等技术对其进行预处理。利用神经网络对柑桔图像的病斑进行划分，通过特征匹配得到准确的识别结果。对比实验结果表明，与传统识别方法相比，所提方法的识别率提高了约 11.9%，性能更好。该方法可以在一定程度上克服外部环境的干扰，为柑桔病虫害的防治提供参考数据。
