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In the last few decades, we have witnessed an increasing focus on safety in the workplace. ICT has always played a leading role in this context. One ICT sector that is increasingly important in ensuring safety at work is the Internet of Things and, in particular, the new architectures referring to it, such as SIoT, MIoT and Sentient Multimedia Systems. All these architectures handle huge amounts of data to extract predictive and prescriptive information. For this purpose, they often make use of Machine Learning. In this paper, we propose a framework that uses both Sentient Multimedia Systems and Machine Learning to support safety in the workplace. After the general presentation of the framework, we describe its specialization to a particular case, i.e., fall detection. As for this application scenario, we describe a Machine Learning based wearable device for fall detection that we designed, built and tested. Moreover, we illustrate a safety coordination platform for monitoring the work environment, activating alarms in case of falls, and sending appropriate advices to help workers involved in falls.

The scarcity of water due to climate change is endangering worldwide the production, quality, and economic viability of growing wine grapes. One of the main mitigation measures to be adopted in the viticulture sector will be an adequate irrigation strategy. Irrigation involves an increasing demand for water, a natural limited resource with increasing availability problems for the foreseeable future. Therefore, the development of a precision irrigation system, which is able to manage the efficient use of water and to monitor the crop water stress, is an important research topic for viticulture. This paper, through the analysis of a case study, aims to describe the prototype of a software platform that integrates data coming from different innovative remote and proximal sensors to monitor the hydric stress status of the vineyard. In addition, by using a cost analysis of grape cultivation and implementing economic indices, this study examines the conditions by which irrigation strategies may be economically justified, helping the decision-making process. By combining different sensors, the platform makes it possible to assess the spatial and temporal variability of water in vineyards. In addition, the output data of the platforming, matched with the economic indices, support the decision-making process for winemakers to optimize and schedule water use under water-scarce conditions.

Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the Mezzano Lowland (Italy), a 189 km2 agricultural plain investigated through a dedicated airborne gamma-ray spectroscopy survey. The K and Th abundances were used to retrieve the clay and sand content by means of a multi-approach method. Linear (simple and multiple) and non-linear (machine learning algorithms with deep neural networks) predictive models were trained and tested adopting a 1:50,000 scale soil texture map. The comparison of these approaches highlighted that the non-linear model introduces significant improvements in the prediction of soil texture fractions. The predicted maps of the clay and of the sand content were compared with the regional soil maps. Although the macro-structures were equally present, the airborne gamma-ray data permits us shedding light on finer features. Map areas with higher clay content were coincident with paleo-channels crossing the Mezzano Lowland in Etruscan and Roman periods, confirmed by the hydrographic setting of historical maps and by the geo-morphological features of the study area.

Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the Mezzano Lowland (Italy), a 189 km2 agricultural plain investigated through a dedicated airborne gamma-ray spectroscopy survey. The K and Th abundances were used to retrieve the clay and sand content by means of a multi-approach method. Linear (simple and multiple) and non-linear (machine learning algorithms with deep neural networks) predictive models were trained and tested adopting a 1:50,000 scale soil texture map. The comparison of these approaches highlighted that the non-linear model introduces significant improvements in the prediction of soil texture fractions. The predicted maps of the clay and of the sand content were compared with the regional soil maps. Although the macro-structures were equally present, the airborne gamma-ray data permits us shedding light on finer features. Map areas with higher clay content were coincident with paleo-channels crossing the Mezzano Lowland in Etruscan and Roman periods, confirmed by the hydrographic setting of historical maps and by the geo-morphological features of the study area.

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<p>Soil texture is a key information in precision agriculture for improving soil knowledge and crop performances. A precise mapping of its variability is thereby imperative for rationally planning cultivations and targeting interventions. Unlike direct soil texture measurements that are punctual, destructive, and time-consuming, remote sensing surveys can give widespread, non-invasive, and fast indirect evidence of clay, silt, and sand content. In this study we investigate the performance of Airborne Gamma Ray Spectroscopy (AGRS) for discriminating different texture classes in the ternary diagram of soil texture.</p><p>The Mezzano valley (Ferrara, Italy), a 180 km<sup>2</sup> rural area reclaimed in the last century, represents an extraordinary benchmark for validating our method. This area, for which a public soil texture map at 1:50000 scale and a spatial resolution of 500 m is available, was scanned by an AGRS system mounted on a dedicated aircraft. The aircraft flew over the study area in a grid-like path of ~500 m spacing, collecting 1469 geolocalized spectra. The K and Th punctual measurements were spatially interpolated by Ordinary Kriging to elaborate K and Th maps with the identical spatial resolution of the soil texture map. Simple and multiple linear correlations, as well as a non‑linear Machine Learning algorithm, were then performed between gamma and soil texture data.</p><p>The obtained results by a simple linear regression analysis highlight a moderate positive (negative) correlation between clay (sand) content and K and Th abundances. Multiple linear regressions show a similar trend, with the limitation that the calculated clay, silt, and sand values populate the soil texture ternary diagram in a straight line. Finally, we demonstrate that the most accurate reconstruction of soil texture values is obtained by a non-linear fitting based on the Machine Learning algorithm.</p>