A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture
<p dir="ltr">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...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , |
| Published: |
2024
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513543601127424 |
|---|---|
| author | Jalal Uddin Md Akbar (21324617) |
| author2 | Syafiq Fauzi Kamarulzaman (20904512) Abu Jafar Md Muzahid (20904509) Md. Arafatur Rahman (21324404) Mueen Uddin (4903510) |
| author2_role | author author author author |
| author_facet | Jalal Uddin Md Akbar (21324617) Syafiq Fauzi Kamarulzaman (20904512) Abu Jafar Md Muzahid (20904509) Md. Arafatur Rahman (21324404) Mueen Uddin (4903510) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jalal Uddin Md Akbar (21324617) Syafiq Fauzi Kamarulzaman (20904512) Abu Jafar Md Muzahid (20904509) Md. Arafatur Rahman (21324404) Mueen Uddin (4903510) |
| dc.date.none.fl_str_mv | 2024-01-03T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ACCESS.2024.3349418 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Comprehensive_Review_on_Deep_Learning_Assisted_Computer_Vision_Techniques_for_Smart_Greenhouse_Agriculture/29605130 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Agricultural, veterinary and food sciences Crop and pasture production Horticultural production Engineering Biomedical engineering Chemical engineering Control engineering, mechatronics and robotics Environmental sciences Ecological applications Environmental management Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Agricultural automation Computer vision Deep learning Convolutional neural networks(CNN) Controlled-environment agriculture (CEA) Greenhouse farming Smart farming Smart agriculture Precision agriculture Image classification Image segmentation Object detection |
| dc.title.none.fl_str_mv | A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3349418" target="_blank">https://dx.doi.org/10.1109/access.2024.3349418</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_ba8bc0dcb8969c764cbca8cf6335ee70 |
| identifier_str_mv | 10.1109/ACCESS.2024.3349418 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29605130 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse AgricultureJalal Uddin Md Akbar (21324617)Syafiq Fauzi Kamarulzaman (20904512)Abu Jafar Md Muzahid (20904509)Md. Arafatur Rahman (21324404)Mueen Uddin (4903510)Agricultural, veterinary and food sciencesCrop and pasture productionHorticultural productionEngineeringBiomedical engineeringChemical engineeringControl engineering, mechatronics and roboticsEnvironmental sciencesEcological applicationsEnvironmental managementInformation and computing sciencesArtificial intelligenceComputer vision and multimedia computationMachine learningAgricultural automationComputer visionDeep learningConvolutional neural networks(CNN)Controlled-environment agriculture (CEA)Greenhouse farmingSmart farmingSmart agriculturePrecision agricultureImage classificationImage segmentationObject detection<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3349418" target="_blank">https://dx.doi.org/10.1109/access.2024.3349418</a></p>2024-01-03T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ACCESS.2024.3349418https://figshare.com/articles/journal_contribution/A_Comprehensive_Review_on_Deep_Learning_Assisted_Computer_Vision_Techniques_for_Smart_Greenhouse_Agriculture/29605130CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296051302024-01-03T03:00:00Z |
| spellingShingle | A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture Jalal Uddin Md Akbar (21324617) Agricultural, veterinary and food sciences Crop and pasture production Horticultural production Engineering Biomedical engineering Chemical engineering Control engineering, mechatronics and robotics Environmental sciences Ecological applications Environmental management Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Agricultural automation Computer vision Deep learning Convolutional neural networks(CNN) Controlled-environment agriculture (CEA) Greenhouse farming Smart farming Smart agriculture Precision agriculture Image classification Image segmentation Object detection |
| status_str | publishedVersion |
| 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 | A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture |
| topic | Agricultural, veterinary and food sciences Crop and pasture production Horticultural production Engineering Biomedical engineering Chemical engineering Control engineering, mechatronics and robotics Environmental sciences Ecological applications Environmental management Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Agricultural automation Computer vision Deep learning Convolutional neural networks(CNN) Controlled-environment agriculture (CEA) Greenhouse farming Smart farming Smart agriculture Precision agriculture Image classification Image segmentation Object detection |