Application of ISO 25178 standard for multiscale 3D parametric assessment of surface topographies

The objective of the present work is to discuss the potential of areal surface texture parameters as introduced and discussed by ISO standards 25178, as a widely recognized reference framework of indices and procedures, which can help and accelerate understanding of functional information. Such indices have been developed specifically for the micro-scale, however they can be successfully implemented also in the case of larger scales. Parameters extraction takes place in three main steps, independently from the scale: calibration, filtering and parameter extraction. The possibility of using the same approach and the same roughness parameters at different scales helps very much not only the post processing of surfaces data sets but also their interpretation, putting the basis for multiscale models

Medium-resolution multispectral data from sentinel-2 to assess the damage and the recovery time of late frost on Vineyards

In a climate-change context, the advancement of phenological stages may endanger viticultural areas in the event of a late frost. This study evaluated the potential of satellite-based remote sensing to assess the damage and the recovery time after a late frost event in 2017 in northern Italian vineyards. Several vegetation indices (VIs) normalized on a two-year dataset (2018-2019) were compared over a frost-affected area (F) and a control area (NF) using unpaired two-sample t-test. Furthermore, the must quality data (total acidity, sugar content and pH) of F and NF were analyzed. The VIs most sensitive in the detection of frost damage were Chlorophyll Absorption Ratio Index (CARI), Enhanced Vegetation Index (EVI), and Modified Triangular Vegetation Index 1 (MTVI1) (-5.26%,-16.59%, and-5.77% compared to NF, respectively). The spectral bands Near-Infrared (NIR) and Red Edge 7 were able to identify the frost damage (-16.55 and-16.67% compared to NF, respectively). Moreover, CARI, EVI, MTVI1, NIR, Red Edge 7, the Normalized Difference Vegetation Index (NDVI) and the Modified Simple Ratio (MSR) provided precise information on the full recovery time (+17.7%, +22.42%, +29.67%, +5.89%, +5.91%, +16.48%, and +8.73% compared to NF, respectively) approximately 40 days after the frost event. The must analysis showed that total acidity was higher (+5.98%), and pH was lower (-2.47%) in F compared to NF. These results suggest that medium-resolution multispectral data from Sentinel-2 constellation may represent a cost-effective tool for frost damage assessment and recovery management

Monitoring within-field variability of corn yield using sentinel-2 and machine learning techniques

Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4-R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4-R6)

New Perspectives on the Sanctuary of Aesculapius in Nora (Sardinia): From Photogrammetry to Visualizing and Querying Tools

The ritual space of the Sanctuary of Aesculapius in Nora (Sardinia) is the main focus of a recent archaeological campaign led by the Cultural Heritage Department of the University of Padova. A partnership with 3DOM research group (Fondazione Bruno Kessler, Trento) has offered new opportunities for a digital investigation of the site. The aim of the project is to map and visualize the sanctuary with methodologies enabling different users to engage with the site in new ways. They offer different web tools for exploring, understanding and interacting with the site, by focusing on 3D modelling, semantic enrichment and the contextualization of digital records. The entire site of Nora has been surveyed by a drone, which produced a digital model of the peninsula. A number of outputs have been used for different scales of visualization and a range of purposes: an open source multi-resolution web renderer is used to navigate the point cloud, labelled using a system of bounding boxes. At the same time it provides access to a 2.5D model of each building. Plugins in QGIS are used to produce extrusions of any mapped feature, gaining height values from the point cloud, and attributes from the shapefile. Photogrammetric models of single ritual artifacts can be located in their own context and be displayed using 3D web renderers

Physical parameters kinetics during the drying process of quarters and halves cut tomatoes

Received: January 27th, 2023 ; Accepted: May 8th, 2023 ; Published: May 19th, 2023 ; Correspondence: [email protected] drying is a time-consuming industrial process. Moreover, the prolonged use of high temperatures decreases the quality of tomatoes and increases the environmental footprint of the process. In most cases, drying is performed on halved tomatoes. Alternatively, the use of quarter tomatoes could guarantee a drying times reduction without compromising the final product quality. This work aimed at modelling changes in physical characteristics of half and quarter tomatoes. The drying tests were conducted at 50 and 60 °C. The kinetics of weight loss, colour change, and volume reduction were determined. Colour change was monitored through image analysis, while volume reduction using RGB-D reconstructions. Based on the results, an increase in the drying temperature and the use of quartered tomatoes allow a significant reduction in drying times. The loss of water kinetic allowed the determination of critical moisture. Between initial and critical moisture, loss of water occurred at constant rate (zero-order kinetic), while after that the rate decreased exponentially (first-order kinetic). The colour kinetics showed an initial constant rate followed by a linear increase for brown pixels. The variation of red pixels did not have a clear trend. Increasing the temperature there was no significant reduction in colour quality while quarter tomatoes showed a greater loss of redness than halved tomatoes. Furthermore, the temperature increase does not affect the volume reduction of the tomatoes. Increasing the temperature and the use of quartered tomatoes are simple solutions to reduce drying times. However, quartered tomatoes are less visually appreciable than halved tomatoes

Exposure to Air Pollution in Transport Microenvironments

People spend approximately 90% of their day in confined spaces (at home, work, school or in transit). During these periods, exposure to high concentrations of atmospheric pollutants can pose serious health risks, particularly to the respiratory system. The objective of this paper is to define a framework of the existing literature on the assessment of air quality in various transport microenvironments. A total of 297 papers, published from 2002 to 2021, were analyzed with respect to the type of transport microenvironments, the pollutants monitored, the concentrations measured and the sampling methods adopted. The analysis emphasizes the increasing interest in this topic, particularly regarding the evaluation of exposure in moving cars and buses. It specifically focuses on the exposure of occupants to atmospheric particulate matter (PM) and total volatile organic compounds (TVOCs). Concentrations of these pollutants can reach several hundreds of µg/m3 in some cases, significantly exceeding the recommended levels. The findings presented in this paper serve as a valuable resource for urban planners and decision-makers in formulating effective urban policies

Order Picking Systems: A Queue Model for Dimensioning the Storage Capacity, the Crew of Pickers, and the AGV Fleet

Designing an order picking system can be very complex, as several interrelated control variables are involved. We address the sizing of the storage capacity of the picking bay, the crew of pickers, and the AGV fleet, which are the most important variables from a tactical viewpoint in a parts-to-pickers system. Although order picking is a widely explored topic in the literature, no analytical model that can simultaneously deal with these variables is currently available. To bridge this gap, we introduce a queue model for Markovian processes, which enables us to jointly optimise the aforementioned control variables. A discrete-event simulation is then used to validate our model, and we then test our proposal with real data under different operative scenarios, with the aim of assessing the usefulness of the proposal in real settings

Weak and Strong Sustainability of Irrigation: A Framework for Irrigation Practices Under Limited Water Availability

Agriculture strongly relies on irrigation. While irrigated land accounts for roughly 20% of the global cultivated area, it contributes to about 40% of crop production. In the last few decades, the growing demand for agricultural commodities has translated into an increasing pressure on the global freshwater resources, often leading to their unsustainable use. Here we investigate the sustainability of irrigation, balancing farmers' profit generation objectives and the needs of ecological systems. We ask the question “sustainability of what?” to stress how the sustainability of irrigation is often evaluated with respect the opposing needs of humans and nature. While from the farmers' perspective irrigation is sustainable when it provides uninterrupted access to water resources at a price not exceeding the marginal revenue they generate (clearly without accounting for environmental externalities), from the standpoint of water resources, irrigation is sustainable if it does not deplete freshwater stocks or environmental flows. We invoke the notions of “weak” and “strong” sustainability to develop a novel framework for the evaluation of tradeoffs between human needs and the conservation of natural capital. Through the analysis of criteria of performance, we relate water deficit and irrigation overuse to the reliability and resilience of irrigation. This approach is applied to the case of Australia, a major agricultural country affected by water scarcity. The application of the framework to the case of Australia shows how this approach can be used to highlight areas in which irrigation contributes to a weakly sustainable use of water resources with impacts on environmental flows and groundwater stocks. Solutions, such as increasing efficiencies or reducing water applications through the adoption of deficit irrigation, can enhance water sustainability in some water scarce locations

wGrapeUNIPD-DL: An open dataset for white grape bunch detection

National and international Vitis variety catalogues can be used as image datasets for computer vision in viticulture. These databases archive ampelographic features and phenology of several grape varieties and plant structures images (e.g. leaf, bunch, shoots). Although these archives represent a potential database for computer vision in viticulture, plant structure images are acquired singularly and mostly not directly in the vineyard. Localization computer vision models would take advantage of multiple objects in the same image, allowing more efficient training. The present images and labels dataset was designed to overcome such limitations and provide suitable images for multiple cluster identification in white grape varieties. A group of 373 images were acquired from later view in vertical shoot position vineyards in six different Italian locations at different phenological stages. Images were then labelled in YOLO labelling format. The dataset was made available both in terms of images and labels. The real number of bunches counted in the field, and the number of bunches visible in the image (not covered by other vine structures) was recorded for a group of images in this dataset