Federico Carotenuto

Gold kiwifruits from two different farms, harvested at different times, were analysed using both non-destructive and destructive methods. A computer vision system (CVS) and a portable spectroradiometer were used to perform non-destructive measurements of firmness, titratable acidity, pH, soluble solids content, dry matter, and soluble sugars (glucose and fructose), with the goal of building predictive models for the maturity index. Hyperspectral data from the visible–near-infrared (VIS–NIR) and short-wave infrared (SWIR) ranges, collected via the spectroradiometer, along with colour features extracted by the CVS, were used as predictors. Three different regression methods—Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian process regression (GPR)—were tested to assess their predictive accuracy. The results revealed a significant increase in sugar content across the different harvesting times in the season. Regardless of the regression method used, the CVS was not able to distinguish among the different harvests, since no significant skin colour changes were measured. Instead, hyperspectral measurements from the near-infrared (NIR) region and the initial part of the SWIR region proved useful in predicting soluble solids content, glucose, and fructose. The models built using these spectral regions achieved R² average values between 0.55 and 0.60. Among the different regression models, the GPR-based model showed the best performance in predicting kiwifruit soluble solids content, glucose, and fructose. In conclusion, for the first time, the effectiveness of a fully portable spectroradiometer measuring surface reflectance until the full SWIR range for the rapid, contactless, and non-destructive estimation of the maturity index of kiwifruits was reported. The versatility of the portable spectroradiometer may allow for field applications that accurately identify the most suitable moment to carry out the harvesting.

Urban heat waves are emerging as a significant public health issue, especially considering their regularity and growing severity, partly induced by climate change. Described as an extended period of very hot weather, which can stretch over several days and weeks, urban heat waves tend to present an increase in occurrence and intensity over time. The elderly and disabled are at greater risk among these populations due to a complex of physiologic, socio-economic, and environmental predisposing conditions. It is essential to know what problems members of these populations face during periods of extreme heat, as the implications are not simply uncomfortable but also involve adverse health effects and higher mortality rates. Even though devoted policies for the elderly and disabled have been developed, they appear mostly sectoral, without a clear link to the structure of the cities and the regenerative policy approaches necessary to properly implement Target 11 of the UN Agenda 2030. European policies often refer to the ‘no one left behind’; the needs of the elderly and the disabled should be deeply considered, and a multidisciplinary approach should be applied in any single step of the urban design.

Free air carbon dioxide enrichment (FACE) offers a unique approach to study the response of crops to rising carbon dioxide (CO₂) concentrations under field conditions. We have established a Miglietta‐type FACE facility in Northern Germany (JKI‐FACE) consisting of four rings with CO₂ fumigation and four rings without fumigation with diameters of 15.5 m. Here, we present the technical details and the performance of the JKI‐FACE facility based on thee‐year data (2022–2024) regarding temporal and spatial carbon dioxide distribution. Our results indicated that high‐frequency (1‐min) data of CO₂ concentration was within 10% and 20% of the target concentration (600 ppm) for 84.2% and 95.8% of the time, respectively. Although the rings are relatively close together in our experimental setup, CO₂ drift measurements suggest no significant interference between the rings. These results suggest that JKI‐FACE is a suitable field infrastructure for studying the effects of increasing CO₂ concentrations in cereals. With this FACE facility, we focus on the intraspecific variation of the response of crop yield and quality to elevated CO₂ to make an important contribution to the adaptation of crops and cropping systems to changing climate conditions.

Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM_2.5 and PM₁₀ daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM_2.5 and 20 for PM₁₀ concentrations were compared vs. regulatory-grade stations during a full “heating season” (15 October 2022–15 April 2023). Both LC sensor networks showed higher accuracy in fitting the magnitude of PM₁₀ than PM_2.5 reference observations, while lower accuracy was shown in terms of RMSE, MAE and R². AirQino stations under-estimated both PM_2.5 and PM₁₀ reference concentrations (MB = −4.8 and −2.9 μg/m³, respectively), while PurpleAir stations over-estimated PM_2.5 concentrations (MB = +5.4 μg/m³) and slightly under-estimated PM₁₀ concentrations (MB = −0.4 μg/m³). PurpleAir stations were finer than AirQino at capturing the time variation of both PM_2.5 and PM₁₀ daily concentrations (R² = 0.68–0.75 vs. 0.59–0.61). LC sensors from both monitoring networks failed to capture the magnitude and dynamics of the PM_2.5/PM₁₀ ratio, confirming their well-known issues in correctly discriminating the size of individual particles. These findings suggest the need for further efforts in the implementation of mass conversion algorithms within LC units to improve the tuning of PM_2.5 vs. PM₁₀ outputs.

This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP’s modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles.

Agrivoltaics is presented as a possible solution to the need for new sources of renewable energies, also responding to the increasing demand for feed/food and energy in a strongly efficient and sustainable manner. To this aim, agrivoltaics proposes to combine agricultural and renewable energy production on the same land using photovoltaic technology. The performance of this new production model strongly depends on the interaction between the two systems, agricultural and photovoltaic. In that sense, one of the most important aspects to consider are the effects of the shadows of the photovoltaic panels on the crop land. Overall, the experiment clearly indicated that a fourth cycle of escarole is possible under the PVs of agrivoltaics. Both fresh weight and size of the salad bowls were significantly increased by the shade provided by the PVs. Escarole appeared to be very tolerant to the shade and commercial yields were boosted, compared to full sun treatments, even under extended shade conditions. Such an effect can be likely explained by an overall amelioration of the water status in shaded plots. Therefore, a further study of the behavior of escarole under agrivoltaic conditions will be desirable.

Lakes in the Alpine region are recognized as critical methane (CH₄) emitters, but a robust characterization of the magnitude and variability of CH₄ fluxes is missing. We developed a mobile platform for CH₄ eddy covariance (EC) flux measurements to tackle this gap. The mobile system was deployed at nine lakes across a latitudinal transect in the Alps and validated by comparing the measured fluxes with fixed on‐shore EC station and to chamber and boundary layer flux estimates. Our approach was shown to be well suited to capture different CH₄ emission pathways and to integrate the within lake variability, therefore, overcoming the limitations of other methods (e.g., boundary layer method). Additionally, this mobile system offers a tool to characterize inter‐lake variability of fluxes with consistent measurements. Methane fluxes were explained by dissolved nitrogen, total phosphorus, dissolved oxygen, seston and lake size. The highest fluxes and most substantial seasonal variability were found in a shallow low‐altitude lake in the Southern Alps. We suggest that characterizing the intra‐lake emission heterogeneity and consistent measurements for a better understanding of inter‐lake emission differences is fundamental for a solid estimate of freshwater CH₄ budgets.