Enhancing image annotation technique of fruit classification using a deep learning approach

Mamat, Normaisharah and Othman, Mohd Fauzi and Abdulghafor, Rawad Abdulkhaleq Abdulmolla and Alwan, Ali A. and Gulzar, Yonis (2023) Enhancing image annotation technique of fruit classification using a deep learning approach. Sustainability, 15 (2). pp. 1-19. ISSN 2071-1050

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Abstract

An accurate image retrieval technique is required due to the rapidly increasing number of images. It is important to implement image annotation techniques that are fast, simple, and, most importantly, automatically annotate. Image annotation has recently received much attention due to the massive rise in image data volume. Focusing on the agriculture field, this study implements automatic image annotation, namely, a repetitive annotation task technique, to classify the ripeness of oil palm fruit and recognize a variety of fruits. This approach assists farmers to enhance the classification of fruit methods and increase their production. This study proposes simple and effective models using a deep learning approach with You Only Look Once (YOLO) versions. The models were developed through transfer learning where the dataset was trained with 100 images of oil fruit palm and 400 images of a variety of fruit in RGB images. Model performance and accuracy of automatically annotating the images with 3500 fruits were examined. The results show that the annotation technique successfully annotated a large number of images accurately. The mAP result achieved for oil palm fruit was 98.7% and the variety of fruit was 99.5%.

Item Type:	Article (Journal)
Uncontrolled Keywords:	image annotation; repetitive annotation task; large dataset; oil palm FFB; classification
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr. Rawad Abdulghafor
Date Deposited:	08 Mar 2023 12:46
Last Modified:	08 Mar 2023 13:22
URI:	http://irep.iium.edu.my/id/eprint/103861

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