Deep learning sensor fusion in plant water stress assessment: a comprehensive review
Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preven...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/96605/1/ABSTRACT.pdf |
| _version_ | 1796983266645901312 |
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| author | Kamarudin, Mohd Hider Ismail, Zool Hilmi Saidi, Noor Baity |
| author_facet | Kamarudin, Mohd Hider Ismail, Zool Hilmi Saidi, Noor Baity |
| author_sort | Kamarudin, Mohd Hider |
| collection | UPM |
| description | Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations. |
| first_indexed | 2024-03-06T11:04:05Z |
| format | Article |
| id | upm.eprints-96605 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T11:04:05Z |
| publishDate | 2021 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | upm.eprints-966052023-01-11T06:57:19Z http://psasir.upm.edu.my/id/eprint/96605/ Deep learning sensor fusion in plant water stress assessment: a comprehensive review Kamarudin, Mohd Hider Ismail, Zool Hilmi Saidi, Noor Baity Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations. Multidisciplinary Digital Publishing Institute 2021 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/96605/1/ABSTRACT.pdf Kamarudin, Mohd Hider and Ismail, Zool Hilmi and Saidi, Noor Baity (2021) Deep learning sensor fusion in plant water stress assessment: a comprehensive review. Applied Sciences-Basel, 11 (4). pp. 1-20. ISSN 2076-3417 https://www.mdpi.com/2076-3417/11/4/1403 10.3390/app11041403 |
| spellingShingle | Kamarudin, Mohd Hider Ismail, Zool Hilmi Saidi, Noor Baity Deep learning sensor fusion in plant water stress assessment: a comprehensive review |
| title | Deep learning sensor fusion in plant water stress assessment: a comprehensive review |
| title_full | Deep learning sensor fusion in plant water stress assessment: a comprehensive review |
| title_fullStr | Deep learning sensor fusion in plant water stress assessment: a comprehensive review |
| title_full_unstemmed | Deep learning sensor fusion in plant water stress assessment: a comprehensive review |
| title_short | Deep learning sensor fusion in plant water stress assessment: a comprehensive review |
| title_sort | deep learning sensor fusion in plant water stress assessment a comprehensive review |
| url | http://psasir.upm.edu.my/id/eprint/96605/1/ABSTRACT.pdf |
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