Plant Disease Diagnosing Based on Deep Learning  Techniques:

Saman M. Omer; Kayhan Z. Ghafoor; Shavan K. Askar

doi:10.14500/aro.11080

Saman M. Omer (1) Department of Technical Information System Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Kurdistan Region – F.R. Iraq; (2) Department of Computer Science, College of Basic Education, University of Raparin, Ranya, Kurdistan Region – F.R. Iraq https://orcid.org/0000-0002-5517-9066
Kayhan Z. Ghafoor (1) Department of Computer Science, Knowledge University, Erbil 44001, Kurdistan Region – F.R. Iraq; (4) Department of Software and Informatics Engineering, Salahaddin University-Erbil, Erbil 44001, Kurdistan Region – F.R. Iraq https://orcid.org/0000-0001-9046-9475
Shavan K. Askar Department of Technical Information System Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Kurdistan Region – F.R. Iraq https://orcid.org/0000-0002-9279-8181

Keywords: Deep learning,, Plant disease classification,, Plant disease detection,, Plant disease recognition

Abstract

Agriculture crops are highly significant for the sustenance of human life and act as an essential source for national income development worldwide. Plant diseases and pests are considered one of the most imperative factors influencing food production, quality, and minimize losses in production. Farmers are currently facing difficulty in identifying various plant diseases and pests, which are important to prevent plant diseases effectively in a complicated environment. The recent development of deep learning techniques has found use in the diagnosis of plant diseases and pests, providing a robust tool with highly accurate results. In this context, this paper presents a comprehensive review of the literature that aims to identify the state of the art of the use of convolutional neural networks (CNNs) in the process of diagnosing and identification of plant pest and diseases. In addition, it presents some issues that are facing the models performance, and also indicates gaps that should be addressed in the future. In this regard, we review studies with various methods that addressed plant disease detection, dataset characteristics, the crops, and pathogens. Moreover, it discusses the commonly employed five-step methodology for plant disease recognition, involving data acquisition, preprocessing, segmentation, feature extraction, and classification. It discusses various deep learning architecture-based solutions that have a faster convergence rate of plant disease recognition. From this review, it is possible to understand the innovative trends regarding the use of CNN’s algorithms in the plant diseases diagnosis and to recognize the gaps that need the attention of the research community.

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Author Biographies

Saman M. Omer, (1) Department of Technical Information System Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Kurdistan Region – F.R. Iraq; (2) Department of Computer Science, College of Basic Education, University of Raparin, Ranya, Kurdistan Region – F.R. Iraq

Saman M. Omer is a Lecturer at the Department of Computer Science, College of Basic Education, University of Raparin. He got the B.Sc. degree in Software Engineering and the M.Sc. degree in Advanced Software Engineering. His research interests are in Machine Learning, Deep Learning and Speech Processing.

Kayhan Z. Ghafoor , (1) Department of Computer Science, Knowledge University, Erbil 44001, Kurdistan Region – F.R. Iraq; (4) Department of Software and Informatics Engineering, Salahaddin University-Erbil, Erbil 44001, Kurdistan Region – F.R. Iraq

Kayhan Z. Ghafoor is an associate professor at Salahaddin University-Erbil and avisiting scholar at the University of Wolverhampton. Before that, he was a postdoctoral research fellow at Shanghai Jiao Tong University, where he contributed to two research projects funded by National Natural Science Foundation of China and National Key Research and Development Program. He is also served as a visiting researcher at University Technology Malaysia. He received the B.Sc. degree in electrical engineering, the M.Sc. degree in remote weather monitoring and the Ph.D. degree in wireless networks in 2003, 2006, and 2011, respectively. He is the recipient of the UTM Chancellor Award at the 48th UTM convocation in 2012.

Shavan K. Askar , Department of Technical Information System Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Kurdistan Region – F.R. Iraq

Shavan Askar is an Assistant Professor at the department of Information system engineering, Erbil technical engineering college, Erbil polytechnic university. He got the B.Sc. degree in computing engineering, the M.Sc. degree in computer engineering and the Ph.D. degree in computer networks. His research interests are in SDN, 5G, IoT, VANET, IoT, AI, Security. Dr. Askar is a member of IEEE Benelux.

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Plant Disease Diagnosing Based on Deep Learning Techniques

A Survey and Research Challenges

Abstract

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Author Biographies

References