This study analyzes, through remote sensing techniques and innovative clouding services, the recent land use dynamics in the North-Roman littoral zone, an area where the latest development has witnessed an important reconversion of purely rural areas to new residential and commercial services. The survey area includes five municipalities and encompasses important infrastructure, such as the “Leonardo Da Vinci” Airport and the harbor of Civitavecchia. The proximity to the metropolis, supported by an efficient network of connections, has modified the urban and peri-urban structure of these areas, which were formerly exclusively agricultural. Hereby, urban expansion has been quantified by classifying Landsat satellite images using the cloud computing platform “Google Earth Engine” (GEE). Landsat multispectral images from 1985 up to 2020 were used for the diachronic analysis, with a five-yearly interval. In order to achieve a high accuracy of the final result, work was carried out along the temporal dimension of the images, selecting specific time windows for the creation of datasets, which were adjusted by the information related to the NDVI index variation through time. This implementation showed interesting improvements in the model performance for each year, suggesting the importance of the NDVI standard deviation parameter. The results showed an increase in the overall accuracy, being from 90 to 97%, with improvements in distinguishing urban surfaces from impervious surfaces. The final results highlighted a significant increase in the study area of the “Urban” and “Woodland” classes over the 35-year time span that was considered, being 67.4 km² and 70.4 km², respectively. The accurate obtained results have allowed us to quantify and understand the landscape transformations in the area of interest, with particular reference to the dynamics of urban development.

The need for accurate information and the availability of novel tool and technological advances in agriculture have given rise to innovative autonomous systems. The aim is to monitor key parameters for optimal water and fertilizer management. A key issue in precision agriculture is the in situ monitoring of soil macronutrients. Here, a proof-of-concept study was conducted that tested two types of sensors capable of capturing both the electrochemical response of the soil and the electrical potential generated by the interaction between the soil and plants. These two sensors can be used to monitor large areas using a network approach, due to their small size and low power consumption. The voltammetric sensor (BIONOTE-L) proved to be able to characterize different soil samples. It was able, indeed, to provide a reproducible voltammetric fingerprint specific for each soil type, and to monitor the concentration of CaCl₂ and NaCl in the soil. BIONOTE-L can be coupled to a device capable of capturing the energy produced by interactions between plants and soil. As a consequence, the functionality of the microsystem node when applied in a large-area monitoring network can be extended. Additional calibrations will be performed to fully characterize the instrument node, to implement the network, and to specialize it for a particular application in the field.

The production of “Nocciola Romana” hazelnuts in the province of Viterbo, Italy, has evolved into a highly efficient and profitable agro-industrial system. Our approach is based on a hierarchical framework utilizing aggregated data from multiple temporal data and sources, offering valuable insights into the spatial, temporal, and phenological distributions of hazelnut crops To achieve our goal, we harnessed the power of Google Earth Engine and utilized collections of satellite images from Sentinel-2 and Sentinel-1. By creating a dense stack of multi-temporal images, we precisely mapped hazelnut groves in the area. During the testing phase of our model pipeline, we achieved an F1-score of 99% by employing a Hierarchical Random Forest algorithm and conducting intensive sampling using high-resolution satellite imagery. Additionally, we employed a clustering process to further characterize the identified areas. Through this clustering process, we unveiled distinct regions exhibiting diverse spatial, spectral, and temporal responses. We successfully delineated the actual extent of hazelnut cultivation, totaling 22,780 hectares, in close accordance with national statistics, which reported 23,900 hectares in total and 21,700 hectares in production for the year 2022. In particular, we identified three distinct geographic distribution patterns of hazelnut orchards in the province of Viterbo, confined within the PDO (Protected Designation of Origin)-designated region. The methodology pursued, using three years of aggregate data and one for SAR with a spectral separation clustering hierarchical approach, has effectively allowed the identification of the specific perennial crop, enabling a deeper characterization of various aspects influenced by diverse environmental configurations and agronomic practices.The accurate mapping and characterization of hazelnut crops open opportunities for implementing precision agriculture strategies, thereby promoting sustainability and maximizing yields in this thriving agro-industrial system.

A plant biological system is exposed to external influences. In general, each plant has its characteristics and needs with specific interaction mechanisms adapted to its survival. Interactions between systems can be examined and modeled as energy exchanges of mechanical, chemical or electrical variables. Thus, each specific interaction can be examined by triggering the system via a specific stimulus. The objective of this work was to study a specific stimulus (mechanical stimulation) as a driver of plants and their interaction with the environment. In particular, the experimental design concerns the setting up and testing of an automatic source of mechanical stimuli at different wavelengths, generated by an electromechanical transducer, to induce a micro-interaction in plants (or in parts of them) that produces a specific behavior (hypothesis) of plants. Four different experimental setups were developed for this work, each pursuing the same objective: the analysis of the germination process induced by stimulation by sound waves in the audible range. It can be said that the introduction of sound waves as a stimulant or a brake for the growth of plants can offer significant advantages when used on a large scale in the primary sector, since these effects can be used instead of polluting chemical solutions.

Soil use and its proper management are key elements of sustainable development. However, given the complexity of the issue, it is necessary to address it using an interdisciplinary approach. The proposed work aims to analyze the consequences, in terms of damage assessment, of two different soil management systems of a cereal crop through the use of Life Cycle Assessment (LCA) methodology. One system follows a traditional approach and the other utilizes a Decision Support System (DSS). The long-term impacts on human health, ecosystems, and resource availability are calculated by employing the ReCiPe 2016 endpoint method. The results show notable reductions in resource use and environmental impacts with DSS, with a 41% decrease in damage to human health, a 24% reduction in ecosystem damage, and a 23% reduction in resource use. Hence, implementing new technologies and new management strategies in agriculture can lead to more sustainable management choices and can avoid long-term burdens compared to a traditional approach.