Artificial intelligence, an emerging concept, has successfully been applied to bioenergy systems. However, highly scattered reviews were narrowly associated with either part of bioenergy systems or an isolated technique, and fewer focused on systematic induction in the agricultural context. This study reviewed 96 papers published from 2012 to 2022, focusing on generalising and comparing AI methods in agricultural bioenergy areas. Specifically, this review broke down the object of study of all previous studies into three parts: bioenergy systems, biomass materials, and AI techniques. Additionally, combined with examples of AI applications, it categorised the bioenergy systems into three phases, including (i) biomass feedstock detection, (ii) bioenergy production/process, and (iii) energy usage, for solving problems such as biomass mapping, composition analysis, cultivation monitoring, process optimisation, bioenergy planning, etc. Based on the review, 44 types of AI algorithms and 11 types of datasets were concluded, in which Artificial Neural Network, Random Forest, Support Vector Machine, Intelligence Decision Support System were mainly used for prediction, classification/regression, and optimal decision-making in issues about biomass systems with 58, 15, 13, and 13 algorithms, respectively.
Artificial Intelligence for Biomass Detection, Production and Energy Usage in Rural Areas: A review of Technologies and Applications
Shi Z.;Marinello F.;Pezzuolo A.
2023
Abstract
Artificial intelligence, an emerging concept, has successfully been applied to bioenergy systems. However, highly scattered reviews were narrowly associated with either part of bioenergy systems or an isolated technique, and fewer focused on systematic induction in the agricultural context. This study reviewed 96 papers published from 2012 to 2022, focusing on generalising and comparing AI methods in agricultural bioenergy areas. Specifically, this review broke down the object of study of all previous studies into three parts: bioenergy systems, biomass materials, and AI techniques. Additionally, combined with examples of AI applications, it categorised the bioenergy systems into three phases, including (i) biomass feedstock detection, (ii) bioenergy production/process, and (iii) energy usage, for solving problems such as biomass mapping, composition analysis, cultivation monitoring, process optimisation, bioenergy planning, etc. Based on the review, 44 types of AI algorithms and 11 types of datasets were concluded, in which Artificial Neural Network, Random Forest, Support Vector Machine, Intelligence Decision Support System were mainly used for prediction, classification/regression, and optimal decision-making in issues about biomass systems with 58, 15, 13, and 13 algorithms, respectively.
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