Literature DB >> 35202746

Smart microalgae farming with internet-of-things for sustainable agriculture.

Hooi Ren Lim1, Kuan Shiong Khoo2, Wen Yi Chia3, Kit Wayne Chew4, Shih-Hsin Ho5, Pau Loke Show6.   

Abstract

Agriculture farms such as crop, aquaculture and livestock have begun the implementation of Internet of Things (IoT) and artificial intelligence (AI) technology in improving their productivity and product quality. However, microalgae farming which requires precise monitoring, controlling and predicting the growth of microalgae biomass has yet to incorporate with IoT and AI technology, as it is still in its infancy phase. Particularly, the cultivation stage of microalgae involves many essential parameters (i.e. biomass concentration, pH, light intensity, temperature and tank level) which require precise monitoring as these parameters are important to ensure an effective biomass productivity in the microalgae farming. Besides, the conventional practices in the current process equipment are still powered by electricity, thus further development by integrating IoT into these processes can ease the production process. Further to that, many researchers has studied the machine learning approach for the identification and classification of microalgae. However, there are still limited studies reported on applying machine learning for the application of microalgae industry such as optimising microalgae cultivation for higher biomass productivity. Therefore, the implementation of IoT and AI in microalgae farming can contribute to the development of the global microalgae industry. The purpose of this current review paper focuses on the overview microalgae biomass production process along with the implementation of IoT toward the future of smart farming. To bridge the gap between the conventional and microalgae smart farming, this paper also highlights the insights on the implementation phases of microalgae smart farming starting from the infant stage that involves the installation and programming of IoT hardware. Then, it is followed by the application of machine learning to predict and auto-optimise the microalgae smart farming process. Furthermore, the process setup and detailed overview of microalgae farming with the integration of IoT have been discussed critically. This review paper would provide a new vision of microalgae farming for microalgae researchers and bio-processing industries into the digitalisation industrial era.
Copyright © 2022 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Internet of things; Machine learning; Microalgae; Smart farming

Mesh:

Year:  2022        PMID: 35202746     DOI: 10.1016/j.biotechadv.2022.107931

Source DB:  PubMed          Journal:  Biotechnol Adv        ISSN: 0734-9750            Impact factor:   14.227


  2 in total

1.  Unsupervised feature selection based on incremental forward iterative Laplacian score.

Authors:  Jiefang Jiang; Xianyong Zhang; Jilin Yang
Journal:  Artif Intell Rev       Date:  2022-09-19       Impact factor: 9.588

2.  A low-cost system for monitoring pH, dissolved oxygen and algal density in continuous culture of microalgae.

Authors:  Dung Kim Nguyen; Huy Quang Nguyen; Huyen Thuy T Dang; Viet Quoc Nguyen; Linh Nguyen
Journal:  HardwareX       Date:  2022-08-27
  2 in total

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