Paper Title
Smart Management of Forests using Machine Learning for Energy Purposes
Abstract
Forest is considered a significant source of woody biomass production. Sustainable production of wood, lower
emittance of CO2 from burning, and lower amount of sulfur and heavy metals are the advantages of wood rather than fossil
fuels. The quality and quantity of woody biomass production are a function of some operations including genetic
modifications, high-quality forestry, evaluation, monitoring, storage, and transportation. Due to surveying numerous related
works, it was found that there is a considerable reviewing gap in analyzing and collecting the applications of Machine
Learning in the quality and quantity of woody biomass. To fill this gap in the current work, the above-mentioned operations
are explained followed by the applications of Machine Learning algorithms. Conclusively, Machine Learning and Deep
Learning can be employed in estimating main effective factors on trees growth, classification of seeds, trees, and regions, as
well as providing decision-making tools for farmers or governors, evaluation of biomass, understanding the relation between
the woody bimass internal structure and bio-fuel production, the ultimate and proximate analyses, prediction of wood
contents and dimensions, determination of the proportion of mixed woody materials, monitoring for early disease
identification and classification, classifying trees diseases, estimating evapotranspiration, collecting information about forest
regions and its quality, nitrogen concentration in trees, choosing viable storage sites for storage depots and improving the
solution, classifying different filling levels in silage, estimating acetic acid synthesis and aerobic reactions in silage,
determining crop quantity in silo, estimating the methane production, and monitoring and predicting water content, quality
and quantity of stored biomass, forecasting the demand, path way and on-time performance predicting, truck traffic
predicting, and behavioral analysis and facility planning.
Keywords - Machine Learning; Forest; Woody biomass; SustainableProduction.