Application of Deep Learning for Fig Fruit Detection in the Wild

This paper proposes an appropriate detection approach based on a deep learning methodology to accomplish fast and effective detection and localization of fig fruits in complicated environmental pictures, e.g., in the wild. A dataset is manually created by first taking pictures of fig fruits in their...

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Published in:2022 2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022
Main Author: Kamaruzaman A.; Farid M.; Izhar C.A.A.; Maruzuki M.; Ahmad S.; Habibi M.A.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142424524&doi=10.1109%2feSmarTA56775.2022.9935356&partnerID=40&md5=739797dddcd67d7b45939fd7cc1fdd4f
id 2-s2.0-85142424524
spelling 2-s2.0-85142424524
Kamaruzaman A.; Farid M.; Izhar C.A.A.; Maruzuki M.; Ahmad S.; Habibi M.A.
Application of Deep Learning for Fig Fruit Detection in the Wild
2022
2022 2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022


10.1109/eSmarTA56775.2022.9935356
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142424524&doi=10.1109%2feSmarTA56775.2022.9935356&partnerID=40&md5=739797dddcd67d7b45939fd7cc1fdd4f
This paper proposes an appropriate detection approach based on a deep learning methodology to accomplish fast and effective detection and localization of fig fruits in complicated environmental pictures, e.g., in the wild. A dataset is manually created by first taking pictures of fig fruits in their natural environment and then annotating them with the label buah tin. The augmentation technique was then employed to prevent overfitting during the training phase. Thus, the dataset can be split into 30% for testing and 70% for training. The YOLOv3 and YOLOv4 one-stage detection deep learning models will be used in the Phyton environment with Google Colaboratory for this research. The experimental outcomes indicated that the YOLOv4 model-based recognition algorithm for detecting fig fruit had outperformed YOLOv3 in terms of average precision and other metrics. Compared to YOLOv3's mAP value of 81.40%, YOLOv4's is the highest at 90.02%. Thus, the YOLOv4 can fulfil essential practical criteria and has a good detecting impact on figs in a challenging environment. © 2022 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper

author Kamaruzaman A.; Farid M.; Izhar C.A.A.; Maruzuki M.; Ahmad S.; Habibi M.A.
spellingShingle Kamaruzaman A.; Farid M.; Izhar C.A.A.; Maruzuki M.; Ahmad S.; Habibi M.A.
Application of Deep Learning for Fig Fruit Detection in the Wild
author_facet Kamaruzaman A.; Farid M.; Izhar C.A.A.; Maruzuki M.; Ahmad S.; Habibi M.A.
author_sort Kamaruzaman A.; Farid M.; Izhar C.A.A.; Maruzuki M.; Ahmad S.; Habibi M.A.
title Application of Deep Learning for Fig Fruit Detection in the Wild
title_short Application of Deep Learning for Fig Fruit Detection in the Wild
title_full Application of Deep Learning for Fig Fruit Detection in the Wild
title_fullStr Application of Deep Learning for Fig Fruit Detection in the Wild
title_full_unstemmed Application of Deep Learning for Fig Fruit Detection in the Wild
title_sort Application of Deep Learning for Fig Fruit Detection in the Wild
publishDate 2022
container_title 2022 2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022
container_volume
container_issue
doi_str_mv 10.1109/eSmarTA56775.2022.9935356
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142424524&doi=10.1109%2feSmarTA56775.2022.9935356&partnerID=40&md5=739797dddcd67d7b45939fd7cc1fdd4f
description This paper proposes an appropriate detection approach based on a deep learning methodology to accomplish fast and effective detection and localization of fig fruits in complicated environmental pictures, e.g., in the wild. A dataset is manually created by first taking pictures of fig fruits in their natural environment and then annotating them with the label buah tin. The augmentation technique was then employed to prevent overfitting during the training phase. Thus, the dataset can be split into 30% for testing and 70% for training. The YOLOv3 and YOLOv4 one-stage detection deep learning models will be used in the Phyton environment with Google Colaboratory for this research. The experimental outcomes indicated that the YOLOv4 model-based recognition algorithm for detecting fig fruit had outperformed YOLOv3 in terms of average precision and other metrics. Compared to YOLOv3's mAP value of 81.40%, YOLOv4's is the highest at 90.02%. Thus, the YOLOv4 can fulfil essential practical criteria and has a good detecting impact on figs in a challenging environment. © 2022 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
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