A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture
With the escalating global challenges of food security and resource sustainability, innovative solutions like deep learning and computer vision are transforming agricultural practices by enabling data-driven decision-making. This paper provides a focused review of recent advancements in deep learnin...
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| Vydáno v: | IEEE access Ročník 12; s. 1 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Piscataway
IEEE
01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2169-3536, 2169-3536 |
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| Abstract | With the escalating global challenges of food security and resource sustainability, innovative solutions like deep learning and computer vision are transforming agricultural practices by enabling data-driven decision-making. This paper provides a focused review of recent advancements in deep learning-enabled computer vision techniques tailored specifically for greenhouse environments. First, deep learning and computer vision fundamentals are briefly introduced. Over 100 studies from 2020 to date are then comprehensively reviewed in which these technologies were applied within greenhouses for growth monitoring, disease detection, yield estimation, and other tasks. The techniques, datasets, models, and overall performance results reported in the literature are analyzed. Tables and figures showcase real-world implementations and results synthesized from current research. Key challenges are also outlined related to aspects like model adaptability, lack of sufficient labeled greenhouse data, computational constraints, the need for multi-modal sensor fusion, and other areas needing further investigation. Future trends and prospects are discussed to provide guidance for researchers exploring computer vision in the niche greenhouse domain. By condensing prior work and elucidating the state-of-the-art, this timely review aims to promote continued progress in smart greenhouse agriculture. The focused analysis, specifically on greenhouse environments, fills a gap compared to previous agricultural surveys. Overall, this paper highlights the immense potential of computer vision and deep learning in driving the emergence of data-driven, smart greenhouse farming worldwide. |
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| AbstractList | With the escalating global challenges of food security and resource sustainability, innovative solutions like deep learning and computer vision are transforming agricultural practices by enabling data-driven decision-making. This paper provides a focused review of recent advancements in deep learning-enabled computer vision techniques tailored specifically for greenhouse environments. First, deep learning and computer vision fundamentals are briefly introduced. Over 100 studies from 2020 to date are then comprehensively reviewed in which these technologies were applied within greenhouses for growth monitoring, disease detection, yield estimation, and other tasks. The techniques, datasets, models, and overall performance results reported in the literature are analyzed. Tables and figures showcase real-world implementations and results synthesized from current research. Key challenges are also outlined related to aspects like model adaptability, lack of sufficient labeled greenhouse data, computational constraints, the need for multi-modal sensor fusion, and other areas needing further investigation. Future trends and prospects are discussed to provide guidance for researchers exploring computer vision in the niche greenhouse domain. By condensing prior work and elucidating the state-of-the-art, this timely review aims to promote continued progress in smart greenhouse agriculture. The focused analysis, specifically on greenhouse environments, fills a gap compared to previous agricultural surveys. Overall, this paper highlights the immense potential of computer vision and deep learning in driving the emergence of data-driven, smart greenhouse farming worldwide. |
| Author | Muzahid, Abu Jafar Md Uddin, Mueen Kamarulzaman, Syafiq Fauzi Rahman, Md Arafatur Akbar, Jalal Uddin Md |
| Author_xml | – sequence: 1 givenname: Jalal Uddin Md surname: Akbar fullname: Akbar, Jalal Uddin Md organization: Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pahang, Malaysia – sequence: 2 givenname: Syafiq Fauzi surname: Kamarulzaman fullname: Kamarulzaman, Syafiq Fauzi organization: Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pahang, Malaysia – sequence: 3 givenname: Abu Jafar Md orcidid: 0000-0003-2344-7027 surname: Muzahid fullname: Muzahid, Abu Jafar Md organization: Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN, USA – sequence: 4 givenname: Md Arafatur orcidid: 0000-0002-8221-6168 surname: Rahman fullname: Rahman, Md Arafatur organization: School of Mathematics and Computer Science, University of Wolverhampton, Wolverhampton, U.K – sequence: 5 givenname: Mueen orcidid: 0000-0003-1919-3407 surname: Uddin fullname: Uddin, Mueen organization: College of Computing and Information Technology, University of Doha for Science and Technology, Doha, Qatar |
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| SubjectTerms | Agricultural automation Agricultural practices Agriculture Air pollution Computer vision Controlled-environment agriculture (CEA) Convolutional neural networks Convolutional neural networks(CNN) Crops Deep learning Farming Green products Greenhouse farming Greenhouses Image classification Image segmentation Object detection Precision agriculture Smart agriculture Smart farming State-of-the-art reviews |
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| Title | A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture |
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