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Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses,...

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Autores principales: Ojo, Mike O., Zahid, Azlan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9612366/
https://www.ncbi.nlm.nih.gov/pubmed/36298316
http://dx.doi.org/10.3390/s22207965
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author Ojo, Mike O.
Zahid, Azlan
author_facet Ojo, Mike O.
Zahid, Azlan
author_sort Ojo, Mike O.
collection PubMed
description Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction. However, no review study assess DL’s state of the art to solve diverse problems in CEA. To fill this gap, we systematically reviewed DL methods applied to CEA. The review framework was established by following a series of inclusion and exclusion criteria. After extensive screening, we reviewed a total of 72 studies to extract the useful information. The key contributions of this article are the following: an overview of DL applications in different CEA facilities, including greenhouse, plant factory, and vertical farm, is presented. We found that majority of the studies are focused on DL applications in greenhouses (82%), with the primary application as yield estimation (31%) and growth monitoring (21%). We also analyzed commonly used DL models, evaluation parameters, and optimizers in CEA production. From the analysis, we found that convolutional neural network (CNN) is the most widely used DL model (79%), Adaptive Moment Estimation (Adam) is the widely used optimizer (53%), and accuracy is the widely used evaluation parameter (21%). Interestingly, all studies focused on DL for the microclimate of CEA used RMSE as a model evaluation parameter. In the end, we also discussed the current challenges and future research directions in this domain.
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spelling pubmed-96123662022-10-28 Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects Ojo, Mike O. Zahid, Azlan Sensors (Basel) Review Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction. However, no review study assess DL’s state of the art to solve diverse problems in CEA. To fill this gap, we systematically reviewed DL methods applied to CEA. The review framework was established by following a series of inclusion and exclusion criteria. After extensive screening, we reviewed a total of 72 studies to extract the useful information. The key contributions of this article are the following: an overview of DL applications in different CEA facilities, including greenhouse, plant factory, and vertical farm, is presented. We found that majority of the studies are focused on DL applications in greenhouses (82%), with the primary application as yield estimation (31%) and growth monitoring (21%). We also analyzed commonly used DL models, evaluation parameters, and optimizers in CEA production. From the analysis, we found that convolutional neural network (CNN) is the most widely used DL model (79%), Adaptive Moment Estimation (Adam) is the widely used optimizer (53%), and accuracy is the widely used evaluation parameter (21%). Interestingly, all studies focused on DL for the microclimate of CEA used RMSE as a model evaluation parameter. In the end, we also discussed the current challenges and future research directions in this domain. MDPI 2022-10-19 /pmc/articles/PMC9612366/ /pubmed/36298316 http://dx.doi.org/10.3390/s22207965 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Ojo, Mike O.
Zahid, Azlan
Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects
title Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects
title_full Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects
title_fullStr Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects
title_full_unstemmed Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects
title_short Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects
title_sort deep learning in controlled environment agriculture: a review of recent advancements, challenges and prospects
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9612366/
https://www.ncbi.nlm.nih.gov/pubmed/36298316
http://dx.doi.org/10.3390/s22207965
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