Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt.
Department of Math and Computer Science Faculty of Science, Port Said University, Port Fuad, Egypt.
Comput Intell Neurosci. 2022 Feb 10;2022:9153207. doi: 10.1155/2022/9153207. eCollection 2022.
Most plant diseases have apparent signs, and today's recognized method is for an expert plant pathologist to identify the disease by looking at infected plant leaves using a microscope. The fact is that manually diagnosing diseases is time consuming and that the effectiveness of the diagnosis is related to the pathologist's talents, making this a great application area for computer-aided diagnostic systems. The proposed work describes an approach for detecting and classifying diseases in citrus plants using deep learning and image processing. The main cause of decreased productivity is considered to be plant diseases, which results in financial losses. Citrus is an important source of nutrients such as vitamin C all around the world. On the contrary, citrus diseases have a negative impact on the citrus fruit and quality. In the recent decade, computer vision and image processing techniques have become increasingly popular for the detection and classification of plant diseases. The suggested approach is evaluated on the citrus disease image gallery dataset and the combined dataset (citrus image datasets of infested scale and plant village). These datasets were used to identify and classify citrus diseases such as anthracnose, black spot, canker, scab, greening, and melanose. AlexNet and VGG19 are two kinds of convolutional neural networks that were used to build and test the proposed approach. The system's total performance reached 94% at its best. The proposed approach outperforms the existing methods.
大多数植物病害都有明显的迹象，目前公认的方法是让植物病理学家通过显微镜观察感染植物的叶片来识别病害。事实上，手动诊断疾病既耗时又费力，而且诊断的有效性还与病理学家的才能有关，这使得计算机辅助诊断系统成为一个极具应用前景的领域。本文提出了一种利用深度学习和图像处理技术检测和分类柑橘植物病害的方法。导致生产力下降的主要原因被认为是植物病害，这会导致经济损失。柑橘是全球维生素 C 等营养物质的重要来源。相反，柑橘病害会对柑橘果实和品质产生负面影响。在过去十年中，计算机视觉和图像处理技术已越来越多地用于植物病害的检测和分类。所提出的方法在柑橘病害图像库数据集和组合数据集（受虫害的柑橘图像数据集和植物村）上进行了评估。这些数据集用于识别和分类炭疽病、黑斑病、溃疡病、疮痂病、黄龙病和褐斑病等柑橘病害。AlexNet 和 VGG19 是两种卷积神经网络，用于构建和测试所提出的方法。该系统的总体性能在最佳情况下达到了 94%。所提出的方法优于现有的方法。
