On Using Deep Artificial Intelligence to Automatically Detect Apple Diseases from Leaf Images

Mohammad Fraiwan (), Esraa Faouri and Natheer Khasawneh
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Mohammad Fraiwan: Department of Computer Engineering, Jordan University of Science and Technology, Ar-Ramtha 3030, Jordan
Esraa Faouri: Department of Computer Engineering, Jordan University of Science and Technology, Ar-Ramtha 3030, Jordan
Natheer Khasawneh: Department of Software Engineering, Jordan University of Science and Technology, Ar-Ramtha 3030, Jordan

Sustainability, 2022, vol. 14, issue 16, 1-14

Abstract: Plant diseases, if misidentified or ignored, can drastically reduce production levels and harvest quality. Technology in the form of artificial intelligence applications has the potential to facilitate and improve the disease identification process, which in turn will empower prompt control. More specifically, the work in this paper addressed the identification of three common apple leaf diseases—rust, scab, and black rot. Twelve deep transfer learning artificial intelligence models were customized, trained, and tested with the goal of categorizing leaf images into one of the aforementioned three diseases or a healthy state. A dataset of 3171 leaf images (621 black rot, 275 rust, 630 scab, and 1645 healthy) was used. Extensive performance evaluation revealed the excellent ability of the transfer learning models to achieve high values (i.e., >99%) for F 1 score, precision, recall, specificity, and accuracy. Hence, it is possible to design smartphone applications that enable farmers with poor knowledge or limited access to professional care to easily identify suspected infected plants.

Keywords: deep learning; scab; black rot; cedar apple rust; image classification; artificial intelligence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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