Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

Normaisharah Mamat, Mohd Fauzi Othman, Rawad Abdoulghafor, Samir Brahim Belhaouari, Normahira Mamat and Shamsul Faisal Mohd Hussein
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Normaisharah Mamat: Department of Electronic System Engineering, Malaysia-Japan International Institute of Technology, University Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
Mohd Fauzi Othman: Department of Electronic System Engineering, Malaysia-Japan International Institute of Technology, University Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
Rawad Abdoulghafor: Computational Intelligence Group Research, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia
Samir Brahim Belhaouari: Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Education City, Doha P.O. Box 34110, Qatar
Normahira Mamat: Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau 02600, Malaysia
Shamsul Faisal Mohd Hussein: Department of Electronic System Engineering, Malaysia-Japan International Institute of Technology, University Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia

Agriculture, 2022, vol. 12, issue 7, 1-35

Abstract: 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.

Keywords: image annotation; deep learning; agriculture; plant recognition; disease detection; counting; classification; yield estimation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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