Automatic Detection and Severity Assessment of Pepper Bacterial Spot Disease via MultiModels Based on Convolutional Neural Networks

Qiufeng Wu, Miaomiao Ji and Zhao Deng
Additional contact information
Qiufeng Wu: Northeast Agricultural University, China
Miaomiao Ji: Northeast Agricultural University, China
Zhao Deng: Northeast Agricultual University, China

International Journal of Agricultural and Environmental Information Systems (IJAEIS), 2020, vol. 11, issue 2, 29-43

Abstract: Pepper bacterial spot disease caused by Xanthomonas campestris is the most common pepper bacterial disease, which ultimately reduces productivity and quality of products. This work uses deep convolutional neural networks (CNNs) to serve fine-grained pepper bacterial spot disease severity classification tasks. The pepper bacterial spot disease leaf images collected from the PlantVillage dataset are further annotated by botanists and split into healthy samples (label1), general samples (label2), and serious samples (label3). To extract more effective and discriminative features, an integrated neural network denoted as MultiModel_VGR is proposed for automatic detection and severity assessment of pepper bacterial spot disease, which is based on three powerful and popular deep learning architectures, namely VGGNet, GoogLeNet and ResNet. Compared with state-of-the-art single CNN architectures and binary-integrated MultiModels, MultiModel_VGR yields the best overall accuracy of 95.34% on the hold-out test dataset, which may have great potential in crop disease control for modern agriculture.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://services.igi-global.com/resolvedoi/resolve ... 18/IJAEIS.2020040103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jaeis0:v:11:y:2020:i:2:p:29-43

Access Statistics for this article

International Journal of Agricultural and Environmental Information Systems (IJAEIS) is currently edited by Frederic Andres

More articles in International Journal of Agricultural and Environmental Information Systems (IJAEIS) from IGI Global
Bibliographic data for series maintained by Journal Editor ().