The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

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Solid biomass fuels are useful and cost effective renewable energy source. The energy content of biomass is determined by its calorific value. The objective of this study was to determine experimentally the gross calorific value (GCV) of different agroforestry species and bio-based industry residues that could be used by: a) companies specialized in processing raw biomass solid biofuel production, b) small-scale consumers (households, medium-sized residential buildings, etc.). The fuel samples used were from agricultural residues and wastes (rice husks, apricot kernels, olive pits, sunflower husks, cotton stems, etc.), energy crops and wetland herbs (cardoon, switchgrass, common reed, narrow-leaf cattail), and forest residues (populus, fagus, pinus). The GCV of the biomass samples was experimentally determined based on CEN/TS 14918:2005, and an oxygen bomb calorimeter was used (Model C5000 Adiabatic Calorimeter, IKA ®-Werke, Staufen, Germany). The GCV of different agroforestry species and residues ranges from 14.3-25.4 MJ•kg −1. The highest GCV was obtained by seeds and kernels due to higher unit mass and higher lipid content. Pinus sylvestris with moisture content 24.59% obtained the lowest GCV (13.973 MJ•kg −1).