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

Author

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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)

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.

Suggested Citation

  • Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdoulghafor & Samir Brahim Belhaouari & Normahira Mamat & Shamsul Faisal Mohd Hussein, 2022. "Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review," Agriculture, MDPI, vol. 12(7), pages 1-35, July.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:7:p:1033-:d:863432
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    References listed on IDEAS

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    1. Jianfang Cao & Aidi Zhao & Zibang Zhang, 2020. "Automatic image annotation method based on a convolutional neural network with threshold optimization," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-21, September.
    2. Nawab Khan & Ram L. Ray & Ghulam Raza Sargani & Muhammad Ihtisham & Muhammad Khayyam & Sohaib Ismail, 2021. "Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture," Sustainability, MDPI, vol. 13(9), pages 1-31, April.
    3. Prakhar Bansal & Rahul Kumar & Somesh Kumar, 2021. "Disease Detection in Apple Leaves Using Deep Convolutional Neural Network," Agriculture, MDPI, vol. 11(7), pages 1-23, June.
    4. Ahmed Kayad & Dimitrios S. Paraforos & Francesco Marinello & Spyros Fountas, 2020. "Latest Advances in Sensor Applications in Agriculture," Agriculture, MDPI, vol. 10(8), pages 1-8, August.
    5. Khadijeh Alibabaei & Pedro D. Gaspar & Tânia M. Lima, 2021. "Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling," Energies, MDPI, vol. 14(11), pages 1-21, May.
    6. Gary Storey & Qinggang Meng & Baihua Li, 2022. "Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture," Sustainability, MDPI, vol. 14(3), pages 1-14, January.
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    Cited by:

    1. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdulghafor & Ali A. Alwan & Yonis Gulzar, 2023. "Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    2. Ewa Ropelewska & Kadir Sabanci & Muhammet Fatih Aslan & Necati Çetin, 2023. "Rapid Detection of Changes in Image Textures of Carrots Caused by Freeze-Drying using Image Processing Techniques and Machine Learning Algorithms," Sustainability, MDPI, vol. 15(8), pages 1-14, April.
    3. Kadir Sabanci & Muhammet Fatih Aslan & Vanya Slavova & Stefka Genova, 2022. "The Use of Fluorescence Spectroscopic Data and Machine-Learning Algorithms to Discriminate Red Onion Cultivar and Breeding Line," Agriculture, MDPI, vol. 12(10), pages 1-11, October.

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