A new method for Espresso Coffee brewing: Caffè Firenze

Espresso coffee is the most popular choice for Italian coffee consumers. It has been estimated that every day, in the world, over of 50 million of Espresso cups are taken. As a consequence of this success, a large number of devices to make Espresso have been developed. In this scenario, a new device has been recently developed and patented (Eu. Patent 06 023 798.9; US 2010/0034942 A1). This brew method, named “Caffè Firenze”, uses a sealed extraction chamber, where water and gas provides pressure higher than the other extraction methods. Three main parts compose the apparatus: the gas source, the extraction chamber and the heat exchanger. The gas source provides the pressured gas required to raise the pressure of the system. The extraction chamber is made with chrome-brass and accessorized with two heating glow plugs. Many are the factors affecting Espresso quality: it is known that, coffee type, roasting conditions and degree, grinding and storage strongly affect the obtained brew. Also, several studies have been carried out on the effect of the setting parameters on quality, for example water pressure, water temperature, and brew time. Among the characteristics that determine Espresso quality, the main attribute for the visual analysis is, without doubts, the foam, also called “crema”. Indeed, height, aspect, and persistency of foam are features much appreciates by consumers. Two distinguish Espresso foam parameters are the persistency and foam index. Equipping a commercial bar machine with the new designed extraction chamber makes feasible the comparison between the traditional way to brew Espresso and the new device. The comparison was made holding the previous mentioned conditions, and differences were evaluated in terms of physical parameters and aromatic profiles. Caffè Firenze shows pronounced differences compared with traditional Espresso in term of foam-related parameters. Also, the new extraction device produces coffees with higher values of body-related parameters, such density and viscosity. The two kinds of Espressos are perceived different at visual analysis and taste by a panel test

AgroShadow: A New Sentinel-2 Cloud Shadow Detection Tool for Precision Agriculture

Remote sensing for precision agriculture has been strongly fostered by the launches of the European Space Agency Sentinel-2 optical imaging constellation, enabling both academic and private services for redirecting farmers towards a more productive and sustainable management of the agroecosystems. As well as the freely and open access policy adopted by the European Space Agency (ESA), software and tools are also available for data processing and deeper analysis. Nowadays, a bottleneck in this valuable chain is represented by the difficulty in shadow identification of Sentinel-2 data that, for precision agriculture applications, results in a tedious problem. To overcome the issue, we present a simplified tool, AgroShadow, to gain full advantage from Sentinel-2 products and solve the trade-off between omission errors of Sen2Cor (the algorithm used by the ESA) and commission errors of MAJA (the algorithm used by Centre National d'Etudes Spatiales/Deutsches Zentrum für Luft- und Raumfahrt, CNES/DLR). AgroShadow was tested and compared against Sen2Cor and MAJA in 33 Sentinel 2A-B scenes, covering the whole of 2020 and in 18 different scenarios of the whole Italian country at farming scale. AgroShadow returned the lowest error and the highest accuracy and F-score, while precision, recall, specificity, and false positive rates were always similar to the best scores which alternately were returned by Sen2Cor or MAJA

On farm agronomic and first environmental evaluation of oil crops for sustainable bioenergy chains

Energy crops, and in particular oil crops, could be an important occasion for developing new non food production rows for a new multi-functional agriculture in Italy. In this view, the use of local biomass is a fundamental starting point for the development of a virtuous energy chain that should pursue not only agricultural profitability, but also chain sustainability and that is less dependent on the global market, characterized by instability in terms of biomass availability and price. From this perspective, particular attention must be paid to crop choice on the basis of its rusticity and of its adaptability to local growing conditions and to low input cropping systems. In this context, alike woody and herbaceous biomasses, oil crops such as sunflower and rapeseed should be able to support local agricultural bioenergy chain in Italy. In addition, in a local bioenergy chain, the role of the farmers should not be limited just to grain production; but also grain processing should be performed at farm or consortium level in oilseed extraction plants well proportioned to the cropped surface. In this way, by means of a simple power generator, farmer could thus produce its own thermal and electric energy from the oil, maximizing his profit. This objective could also be achieved through the exploitation of the total biomass, including crop residues and defatted seed meals, that may be considered as fundamental additional economic and/or environmental benefits of the chain. This paper reports some results of three-years on-farm experiments on oil crop chain carried out in the framework of "Bioenergie" project, that was focused to enhance farmers awareness of these criteria and to the feasibility at open field scale of low-input cultivation of rapeseed, sunflower and Brassica carinata in seven Italian regions. In several on-farm experiences, these crops produced more than 800 kg ha-1 of oil with good energy properties. Defatted seed meals could be interesting as organic fertilizers and, in the case of B. carinata, as a biofumigant amendment that could offer a total or partial alternative to some chemicals in agriculture. Furthermore, biomass soil incorporation could contribute to C sequestration, catching CO2 from atmosphere and sinking a part in soil as stable humus. Finally, four different open field experiences carried out again in the second year of the project, have been analysed in order to evaluate their energy and greenhouse gasses balance after cultivation phase

Bibliometric and Social Network Analysis on the Use of Satellite Imagery in Agriculture: An Entropy-Based Approach

Satellite imagery is gaining popularity as a valuable tool to lower the impact on natural resources and increase profits for farmers. The purpose of this study is twofold: to mine the scientific literature to reveal the structure of this research domain, and to investigate to what extent scientific results can reach a wider public audience. To meet these two objectives, a Web of Science and a Twitter dataset were retrieved and analysed, respectively. For the academic literature, different performances of various countries were observed: the USA and China resulted as the leading actors, both in terms of published papers and employed researchers. Among the categorised keywords, “resolution”, “Landsat”, “yield”, “wheat” and “multispectral” are the most used. Then, analysing the semantic network of the words used in the various abstracts, the different facets of the research in satellite remote sensing were detected. The importance of retrieving meteorological parameters through remote sensing and the broad use of vegetation indexes emerged from these analyses. As emerging topics, classification tasks for land use assessment and crop recognition stand out, alongside the use of hyperspectral sensors. Regarding the interaction of academia with the public, the analysis showed that it is practically absent on Twitter: most of the activity therein stems from private companies advertising their business. This shows that there is still a communication gap between academia and actors from other societal sectors

Bibliometric and Social Network Analysis on the Use of Satellite Imagery in Agriculture: An Entropy-Based Approach

Satellite imagery is gaining popularity as a valuable tool to lower the impact on natural resources and increase profits for farmers. The purpose of this study is twofold: to mine the scientific literature to reveal the structure of this research domain, and to investigate to what extent scientific results can reach a wider public audience. To meet these two objectives, a Web of Science and a Twitter dataset were retrieved and analysed, respectively. For the academic literature, different performances of various countries were observed: the USA and China resulted as the leading actors, both in terms of published papers and employed researchers. Among the categorised keywords, “resolution”, “Landsat”, “yield”, “wheat” and “multispectral” are the most used. Then, analysing the semantic network of the words used in the various abstracts, the different facets of the research in satellite remote sensing were detected. The importance of retrieving meteorological parameters through remote sensing and the broad use of vegetation indexes emerged from these analyses. As emerging topics, classification tasks for land use assessment and crop recognition stand out, alongside the use of hyperspectral sensors. Regarding the interaction of academia with the public, the analysis showed that it is practically absent on Twitter: most of the activity therein stems from private companies advertising their business. This shows that there is still a communication gap between academia and actors from other societal sectors

Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing—A Systematic Review. Part II: Research Applications

Forest sustainable management aims to maintain the income of woody goods for companies, together with preserving non-productive functions as a benefit for the community. Due to the progress in platforms and sensors and the opening of the dedicated market, unmanned aerial vehicle–remote sensing (UAV–RS) is improving its key role in the forestry sector as a tool for sustainable management. The use of UAV (Unmanned Aerial Vehicle) in precision forestry has exponentially increased in recent years, as demonstrated by more than 600 references published from 2018 until mid-2020 that were found in the Web of Science database by searching for “UAV” + “forest”. This result is even more surprising when compared with similar research for “UAV” + “agriculture”, from which emerge about 470 references. This shows how UAV–RS research forestry is gaining increasing popularity. In Part II of this review, analyzing the main findings of the reviewed papers (227), numerous strengths emerge concerning research technical issues. UAV–RS is fully applicated for obtaining accurate information from practical parameters (height, diameter at breast height (DBH), and biomass). Research effectiveness and soundness demonstrate that UAV–RS is now ready to be applied in a real management context. Some critical issues and barriers in transferring research products are also evident, namely, (1) hyperspectral sensors are poorly used, and their novel applications should be based on the capability of acquiring tree spectral signature especially for pest and diseases detection, (2) automatic processes for image analysis are poorly flexible or based on proprietary software at the expense of flexible and open-source tools that can foster researcher activities and support technology transfer among all forestry stakeholders, and (3) a clear lack exist in sensors and platforms interoperability for large-scale applications and for enabling data interoperability

Sentinel-2 Validation for Spatial Variability Assessment in Overhead Trellis System Viticulture Versus UAV and Agronomic Data

Several remote sensing technologies have been tested in precision viticulture to characterize vineyard spatial variability, from traditional aircraft and satellite platforms to recent unmanned aerial vehicles (UAVs). Imagery processing is still a challenge due to the traditional row-based architecture, where the inter-row soil provides a high to full presence of mixed pixels. In this case, UAV images combined with filtering techniques represent the solution to analyze pure canopy pixels and were used to benchmark the effectiveness of Sentinel-2 (S2) performance in overhead training systems. At harvest time, UAV filtered and unfiltered images and ground sampling data were used to validate the correlation between the S2 normalized difference vegetation indices (NDVIs) with vegetative and productive parameters in two vineyards (V1 and V2). Regarding the UAV vs. S2 NDVI comparison, in both vineyards, satellite data showed a high correlation both with UAV unfiltered and filtered images (V1 R2 = 0.80 and V2 R2 = 0.60 mean values). Ground data and remote sensing platform NDVIs correlation were strong for yield and biomass in both vineyards (R2 from 0.60 to 0.95). These results demonstrate the effectiveness of spatial resolution provided by S2 on overhead trellis system viticulture, promoting precision viticulture also within areas that are currently managed without the support of innovative technologies