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Advanced technology in agriculture industry by implementing image annotation technique and deep learning approach: A review

The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of...

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Main Authors: Mamat, Normaisharah, Othman, Mohd. Fauzi, Abdoulghafor, Rawad, Belhaouari, Samir Brahim, Mamat, Normahira, Mohd. Hussein, Shamsul Faisal
Format: Article
Language:English
Published: MDPI 2022
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/100606/1/MohdFauziOthman2022_AdvancedTechnologyinAgricultureIndustrybyImplementingImage.pdf
http://eprints.utm.my/id/eprint/100606/
http://dx.doi.org/10.3390/agriculture12071033
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spelling my.utm.1006062023-04-30T08:13:07Z http://eprints.utm.my/id/eprint/100606/ Advanced technology in agriculture industry by implementing image annotation technique and deep learning approach: A review Mamat, Normaisharah Othman, Mohd. Fauzi Abdoulghafor, Rawad Belhaouari, Samir Brahim Mamat, Normahira Mohd. Hussein, Shamsul Faisal T Technology (General) The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry. As a result, this article discusses the application of image annotation in the agriculture industry utilizing several deep learning approaches. Various types of annotations that were used to train the images are presented. Recent publications have been reviewed on the basis of their application of deep learning with current advancement technology. Plant recognition, disease detection, counting, classification and yield estimation are among the many advancements of deep learning architecture employed in many applications in agriculture that are thoroughly investigated. Furthermore, this review helps to assist researchers to gain a deeper understanding and future application of deep learning in agriculture. According to all of the articles, the deep learning technique has successfully created significant accuracy and prediction in the model utilized. Finally, the existing challenges and future promises of deep learning in agriculture are discussed. MDPI 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/100606/1/MohdFauziOthman2022_AdvancedTechnologyinAgricultureIndustrybyImplementingImage.pdf Mamat, Normaisharah and Othman, Mohd. Fauzi and Abdoulghafor, Rawad and Belhaouari, Samir Brahim and Mamat, Normahira and Mohd. Hussein, Shamsul Faisal (2022) Advanced technology in agriculture industry by implementing image annotation technique and deep learning approach: A review. Agriculture (Switzerland), 12 (7). pp. 1-35. ISSN 2077-0472 http://dx.doi.org/10.3390/agriculture12071033 DOI : 10.3390/agriculture12071033
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Mamat, Normaisharah
Othman, Mohd. Fauzi
Abdoulghafor, Rawad
Belhaouari, Samir Brahim
Mamat, Normahira
Mohd. Hussein, Shamsul Faisal
Advanced technology in agriculture industry by implementing image annotation technique and deep learning approach: A review
description The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry. As a result, this article discusses the application of image annotation in the agriculture industry utilizing several deep learning approaches. Various types of annotations that were used to train the images are presented. Recent publications have been reviewed on the basis of their application of deep learning with current advancement technology. Plant recognition, disease detection, counting, classification and yield estimation are among the many advancements of deep learning architecture employed in many applications in agriculture that are thoroughly investigated. Furthermore, this review helps to assist researchers to gain a deeper understanding and future application of deep learning in agriculture. According to all of the articles, the deep learning technique has successfully created significant accuracy and prediction in the model utilized. Finally, the existing challenges and future promises of deep learning in agriculture are discussed.
format Article
author Mamat, Normaisharah
Othman, Mohd. Fauzi
Abdoulghafor, Rawad
Belhaouari, Samir Brahim
Mamat, Normahira
Mohd. Hussein, Shamsul Faisal
author_facet Mamat, Normaisharah
Othman, Mohd. Fauzi
Abdoulghafor, Rawad
Belhaouari, Samir Brahim
Mamat, Normahira
Mohd. Hussein, Shamsul Faisal
author_sort Mamat, Normaisharah
title Advanced technology in agriculture industry by implementing image annotation technique and deep learning approach: A review
title_short Advanced technology in agriculture industry by implementing image annotation technique and deep learning approach: A review
title_full Advanced technology in agriculture industry by implementing image annotation technique and deep learning approach: A review
title_fullStr Advanced technology in agriculture industry by implementing image annotation technique and deep learning approach: A review
title_full_unstemmed Advanced technology in agriculture industry by implementing image annotation technique and deep learning approach: A review
title_sort advanced technology in agriculture industry by implementing image annotation technique and deep learning approach: a review
publisher MDPI
publishDate 2022
url http://eprints.utm.my/id/eprint/100606/1/MohdFauziOthman2022_AdvancedTechnologyinAgricultureIndustrybyImplementingImage.pdf
http://eprints.utm.my/id/eprint/100606/
http://dx.doi.org/10.3390/agriculture12071033
_version_ 1765296676513251328
score 13.211869

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