The fruit supply chain requires simple, non-destructive, and fast tools for quality evaluation both in the field and during the post-harvest phase. In this study, a portable visible and near-infrared (Vis/NIR) spectrophotometer and a portable Vis/NIR hyperspectral imaging (HSI) device were tested to highlight genetic differences among apricot cultivars, and to develop multi-cultivar and multi-year models for the most important marketable attributes (total soluble solids, TSS; titratable acidity, TA; dry matter, DM). To do this, the fruits of seventeen cultivars from a single experimental orchard harvested at the commercial maturity stage were considered. Spectral data emphasized genetic similarities and differences among the cultivars, capturing changes in the pigment content and macro components of the apricot samples. In recent years, machine learning techniques, such as artificial neural networks (ANNs), have been successfully applied to more efficiently extract valuable information from spectral data and to accurately predict quality traits. In this study, prediction models were developed based on a multilayer perceptron artificial neural network (ANN-MLP) combined with the Levenberg–Marquardt learning algorithm. Regarding the Vis/NIR spectrophotometer dataset, good predictive performances were achieved for TSS (R² = 0.855) and DM (R² = 0.857), while the performance for TA was unsatisfactory (R² = 0.681). In contrast, the optimal predictive ability was found for models of the HSI dataset (TSS: R² = 0.904; DM: R² = 0.918, TA: R² = 0.811), as confirmed by external validation. Moreover, the ANN allowed us to identify the most predictive input spectral regions for each model. The results showed the potential of Vis/NIR spectroscopy as an alternative to traditional destructive methods to monitor the qualitative traits of apricot fruits, reducing the time and costs of analyses.

Beekeeping is a crucial agricultural practice that significantly enhances environmental health and food production through effective pollination by honey bees. However, honey bees face numerous threats, including exotic parasites, large-scale transportation, and common agricultural practices that may increase the risk of parasite and pathogen transmission. A major threat is the Varroa destructor mite, which feeds on honey bee fat bodies and transmits viruses, leading to significant colony losses. Detecting the parasite and defining the intervention thresholds for effective treatment is a difficult and time-consuming task; different detection methods exist, but they are mainly based on human eye observations, resulting in low accuracy. This study introduces a digital portable scanner coupled with an AI algorithm (BeeVS) used to detect Varroa mites. The device works through image analysis of a sticky sheet previously placed under the beehive for some days, intercepting the Varroa mites that naturally fall. In this study, the scanner was tested for 17 weeks, receiving sheets from 5 beehives every week, and checking the accuracy, reliability, and speed of the method compared to conventional human visual inspection. The results highlighted the high repeatability of the measurements (R² ≥ 0.998) and the high accuracy of the BeeVS device; when at least 10 mites per sheet were present, the device showed a cumulative percentage error below 1%, compared to approximately 20% for human visual observation. Given its repeatability and reliability, the device can be considered a valid tool for beekeepers and scientists, offering the opportunity to monitor many beehives in a short time, unlike visual counting, which is done on a sample basis.

Seafood plays a key role in a healthy diet due to its high content of essential nutrients. However, its global trade and complex supply chains expose it to frequent mislabeling and food fraud. This study investigates Italian consumers’ willingness to pay (WTP) for traceable seafood products, exploring how blockchain technology (BT) can enhance transparency and economic sustainability in the fish supply chain. An online questionnaire, administered in 2022 and 2024, gathered responses from a diverse demographic, focusing on four representative seafood species: farmed sea bass, striped venus clams, giant red shrimp, and albacore tuna. Results indicate that most respondents—primarily with higher education levels—value traceability and are willing to pay a premium for certified, traceable seafood. The study models the economic feasibility of implementing BT at both national and regional levels and finds that the consumer’s WTP exceeds the additional costs incurred by adopting BT. These findings support the viability of traceability systems in improving food safety and sustainability, while reinforcing consumer trust. The results also underscore the importance of providing clear information at the point of purchase, particularly regarding species, origin, and production methods—factors critical to informed seafood choices and advancing more sustainable consumer behavior in Italy.

Agricultural soil fertility is a key determinant of crop productivity and long-term sustainability. However, intensive farming practices often require repeated passes of heavy machinery, which can lead to soil compaction. This study examines the interplay between tractor traffic, tire inflation pressure, and their effects on soil physical properties and fertility indicators. Tire pressure management emerges as a crucial mitigation strategy: high inflation pressures concentrate the load and exacerbate subsoil compaction, whereas reduced pressures (within safe limits) enlarge the tire–soil contact area, distributing the vehicle’s weight more evenly. This in turn improves traction, lowers ground pressure, and reduces energy losses. As a result, both the depth and severity of soil compaction are reduced. Further advances may be achieved through innovative tires manufactured with eco-sustainable materials and tread patterns specifically designed to enhance traction and minimize slippage-related energy loss. In this context, CREA conducted comparative field tests on two tractor tire models from the same manufacturer: a conventional design and an evolved version featuring an innovative tread and larger footprint. The trials assessed the impact of each tire on soil compaction, traction performance, and energy efficiency. Tests were performed on a silty-clay agricultural soil naturally settled for a year, using a dynamometric vehicle to apply different controlled traction force levels, combined with two inflation pressure settings. To highlight performance differences between the two models, the tractor was rear-ballasted, and the study focused on the rear axle, which carried most of the traction stress. Results indicated that, under the specific test conditions, at high inflation pressure both tires performed similarly (with the innovative model slightly reducing fuel use and the conventional yielding marginally higher maximum tractive force), whereas at low pressure the innovative tire clearly outperformed the traditional model in traction efficiency and caused less soil compaction. The extent of the benefits associated with using the innovative tire model across various soil conditions, moisture levels, and in the absence of rear ballasting will be evaluated in further tests based on traction force control using the proposed testing system.

Wood fuel from the agroforestry sector is one of the main strategies cited by the EU for reducing energetic dependance on foreign markets. Its sustainability, both economic and environmental, can be improved through the optimization of harvesting and chipping operations. This study was focused on the dynamic and energetic balance of the chipping phase carried out by a chipper operated by the power-take-off (PTO) of a medium-power tractor. Both machines were equipped with sensors for real-time monitoring of fuel consumption, PTO torque and speed, trunk diameter and working time during the comminution of 61 poplar trees grown in a medium rotation coppice system. The data analysis was carried out on the entire dataset (about 29,000 records) without considering their belonging to different trees. By means of proper data ordinations, it has been possible to define all the intervals in which the chipping stopped (e.g., between two trees) and to exclude them from the intervals of actual chipping. This has allowed forcomputation of operative and actual working time, as well as of the basic power required to operate the chipper and the power for actual chipping. Subsequently, the parameter values observed during actual chipping were related to the cutting diameters measured at the same instant. Subsequently, the dataset was divided according to seven diameter classes, and, for each class, the descriptive statistical indices of working time, work productivity, CO₂ emissions, energy requirement and fuel consumption were calculated. Eventually, the correlation between the variations in trunk diameter and other parameters was verified both on the whole dataset and based on the class average values. The analysis made it possible to identify the conditions of greatest efficiency for the chipper. More generally, the method could help to increase the accuracy of measurements aimed at characterizing the performance of chippers from the point of view of dynamic energy requirements as well as in relation to different wood species.