Phasing out fossil fuels to renewables is currently a global priority due to the climate change threat. Advocacy for biomass use as an energy source requires assessing the quality biomass and ecological impacts of bioenergy supply chains. This study evaluated the quality of biomass residues from orchards and silviculture transported from different Northern and Central Italy locations and the carbon footprint of a biomass power plant. The total greenhouse emissions were calculated based on primary data for 2017 according to the ISO/TS 14067. All the residue samples showed their suitability for biofuel use. Ash content was relatively low on average (3–5% d.m.), except for grapevine residues (18% d.m.). The lower heating value was within the expected range of 15–21 MJ kg⁻¹ for plant species. The average GHG emission from the power plant was 17.4 g CO₂ eq./MJ of electrical energy, with the energy conversion (38%) and transportation of biomass (34%) phases being the main impact contributors. For this study, impacts of residual agricultural residue were about half that of residues from forest management, mainly due to chipping and greater transport distance. Results show that sourcing residual biomass materials for electricity generation close to power plants significantly reduce GHG emissions compared to conventional fossil fuels.

Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data.

Biomass materials play a key role in the renewable energy market as they can serve as a suitable alternative to fossil fuels. However, the quality of the material entering bioenergy plants is often a cause of technical concern. Biomass quality assessment is crucial not only for energy characterization but also for environmental and operational aspects. The goal of this study is to characterize and classify the biomasses used by Italian power plants with reference to the quality classes stated by the ISO standard 17225:2021. A further objective is to verify the ability of the standard to classify heterogeneous and specific biomasses. In this study, more than 900 biomass samples were analyzed. The samples were collected from several Italian power plants with >5 MWe between 2010 and 2020, and the most important physical and chemical parameters were analyzed according to the international standards of reference. The results of the analyses were collected in a large dataset used for subsequent statistical analyses. Statistical analyses applied are Principal Component Analysis and Pearson correlation maps, which showed that the ash content is a fundamental and ideal parameter to assess the biomass quality. Results obtained demonstrate that herbaceous biomasses are of low quality mainly due to the high ash content; a relatively low ash content was found for woody biomasses.

Hydrothermal carbonization (HTC) is a promising method for the conversion of agricultural and agro-industrial residues into valuable products. HTC processes biomass through chemical reactions that produce hydrochar, a carbon-rich solid similar to lignite. Unlike other thermochemical processes, HTC can handle high-moisture biomass without pre-drying. This article evaluates the efficiency of HTC on wood chips, wheat straw, and grape pomace, examining their chemical and structural characteristics and critical operational parameters such as the temperature, pressure, biomass/water ratio, and reaction time. The obtained results highlight that the two key process parameters are the temperature and the ratio between the solid biomass and liquid phase. Increasing the first parameter increases the energy content by 20% and increases the carbon concentration by up to 50%, while reducing the oxygen content by 30% in the hydrochar. Varying the second parameter leads to the alternating reduction of the ash content but simultaneously reduces the energy content. The reaction time seems to have a limited influence on the quality parameters of the biochar produced. Lastly, HTC appears to successfully enhance the overall quality of widely available agricultural wastes, such as grape pomace.