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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| Format: | Article |
| Language: | English |
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IEEE
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40096/1/A_Comprehensive_Review_on_Deep_Learning_Assisted_Computer_Vision_Techniques_for_Smart_Greenhouse_Agriculture.pdf |
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| author | Akbar, Jalal Uddin Md Syafiq Fauzi, Kamarulzaman Abu Jafar, Md Muzahid Rahman, Md. Arafatur Uddin, Mueen |
| author_facet | Akbar, Jalal Uddin Md Syafiq Fauzi, Kamarulzaman Abu Jafar, Md Muzahid Rahman, Md. Arafatur Uddin, Mueen |
| author_sort | Akbar, Jalal Uddin Md |
| collection | UMP |
| description | 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. |
| first_indexed | 2024-03-06T13:13:12Z |
| format | Article |
| id | UMPir40096 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:13:12Z |
| publishDate | 2024 |
| publisher | IEEE |
| record_format | dspace |
| spelling | UMPir400962024-01-19T00:38:36Z http://umpir.ump.edu.my/id/eprint/40096/ A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture Akbar, Jalal Uddin Md Syafiq Fauzi, Kamarulzaman Abu Jafar, Md Muzahid Rahman, Md. Arafatur Uddin, Mueen QA75 Electronic computers. Computer science T Technology (General) 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. IEEE 2024 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/40096/1/A_Comprehensive_Review_on_Deep_Learning_Assisted_Computer_Vision_Techniques_for_Smart_Greenhouse_Agriculture.pdf Akbar, Jalal Uddin Md and Syafiq Fauzi, Kamarulzaman and Abu Jafar, Md Muzahid and Rahman, Md. Arafatur and Uddin, Mueen (2024) A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture. IEEE Access, 12. pp. 4485-4522. ISSN 2169-3536. (Published) https://doi.org/10.1109/ACCESS.2024.3349418 10.1109/ACCESS.2024.3349418 |
| spellingShingle | QA75 Electronic computers. Computer science T Technology (General) Akbar, Jalal Uddin Md Syafiq Fauzi, Kamarulzaman Abu Jafar, Md Muzahid Rahman, Md. Arafatur Uddin, Mueen A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture |
| title | A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture |
| title_full | A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture |
| title_fullStr | A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture |
| title_full_unstemmed | A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture |
| title_short | A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture |
| title_sort | comprehensive review on deep learning assisted computer vision techniques for smart greenhouse agriculture |
| topic | QA75 Electronic computers. Computer science T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/40096/1/A_Comprehensive_Review_on_Deep_Learning_Assisted_Computer_Vision_Techniques_for_Smart_Greenhouse_Agriculture.pdf |
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