Rapid Grapevine Health Diagnosis Based on Digital Imaging and Deep Learning

被引:16
作者
Elsherbiny, Osama [1 ,2 ]
Elaraby, Ahmed [3 ,4 ]
Alahmadi, Mohammad [5 ]
Hamdan, Mosab [6 ]
Gao, Jianmin [1 ]
机构
[1] Jiangsu Univ, Sch Agr Engn, Zhenjiang 212013, Peoples R China
[2] Mansoura Univ, Fac Agr, Agr Engn Dept, Mansoura 35516, Egypt
[3] Buraydah Private Coll, Coll Engn & Informat Technol, Dept Cybersecur, Buraydah 51418, Saudi Arabia
[4] South Valley Univ, Fac Comp & Informat, Dept Comp Sci, Qena 83523, Egypt
[5] Univ Jeddah, Coll Comp Sci & Engn, Dept Software Engn, Jeddah 23890, Saudi Arabia
[6] King Fahd Univ Petr & Minerals, Interdisciplinary Res Ctr Intelligent Secure Syst, Dhahran 31261, Saudi Arabia
来源
PLANTS-BASEL | 2024年 / 13卷 / 01期
关键词
grapevine disease; digital imaging; GLCM; hybrid deep networks; transfer learning; standalone software; NETWORKS;
D O I
10.3390/plants13010135
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Deep learning plays a vital role in precise grapevine disease detection, yet practical applications for farmer assistance are scarce despite promising results. The objective of this research is to develop an intelligent approach, supported by user-friendly, open-source software named AI GrapeCare (Version 1, created by Osama Elsherbiny). This approach utilizes RGB imagery and hybrid deep networks for the detection and prevention of grapevine diseases. Exploring the optimal deep learning architecture involved combining convolutional neural networks (CNNs), long short-term memory (LSTM), deep neural networks (DNNs), and transfer learning networks (including VGG16, VGG19, ResNet50, and ResNet101V2). A gray level co-occurrence matrix (GLCM) was employed to measure the textural characteristics. The plant disease detection platform (PDD) created a dataset of real-life grape leaf images from vineyards to improve plant disease identification. A data augmentation technique was applied to address the issue of limited images. Subsequently, the augmented dataset was used to train the models and enhance their capability to accurately identify and classify plant diseases in real-world scenarios. The analyzed outcomes indicated that the combined CNNRGB-LSTMGLCM deep network, based on the VGG16 pretrained network and data augmentation, outperformed the separate deep network and nonaugmented version features. Its validation accuracy, classification precision, recall, and F-measure are all 96.6%, with a 93.4% intersection over union and a loss of 0.123. Furthermore, the software developed through the proposed approach holds great promise as a rapid tool for diagnosing grapevine diseases in less than one minute. The framework of the study shows potential for future expansion to include various types of trees. This capability can assist farmers in early detection of tree diseases, enabling them to implement preventive measures.
引用
收藏
页数:20
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