R-(+)-limonene, one of the major constituents of citrus oils, is a monoterpene that is widely used as a fragrance additive in cosmetics, foods, and industrial solvents. Nowadays, its detection mainly relies on bulky and expensive analytical methods and only a few research works proved its revelation through affordable and portable sensors, such as electrochemical and quartz crystal microbalance sensors. In response to the demand for effective miniaturized sensing devices to be integrated into Internet of Things systems, this study represents a pioneering investigation of chemoresistive gas sensor capabilities addressed to R-(+)-limonene detection. An array of seven metal-oxide sensors was exploited to perform a complete electrical characterization of the target analyte. The experimental evidence allowed us to identify the WO₃-based sensor as the most promising candidate for R-(+)-limonene detection. The material was highly sensitive already at sub-ppm concentrations (response of 2.5 at 100 ppb), consistent with applicative parameters, and it resulted in selective vs. different gases at a lower operating temperature (200 °C) than the other sensors tested. Furthermore, it exhibited a humidity-independent behavior under real-life conditions (relative humidity > 20%). Finally, the WO₃ sensor also demonstrated a remarkable cross-selectivity, thus enabling its exploitation in cutting-edge applications.

Three major technological advances have occurred in recent years in the use of Synthetic Aperture Radar (SAR) for the study of geophysical processes. The first is the launch and excellent performance of ERS satellites, which permits the study of global-scale dynamic processes. The second is the development of airborne polarimetric SARs, which provides afar more complete picture of the scattering properties of the Earth's surface than the "simple" systems flown in space. The third is the inception of interferometric SAR, which allows high resolution topographic data to be generated from spaceborne and airborne SAR systems, and can defect centimetric changes in the Earth's surface. Going hand in hand with these advances, there have been major achievements in backscatter modeling, developing viable statistical models of the data and in image understanding techniques bt this work we study the applications of the above concepts in characterizing "crushed" soils in terms of accuracy assessment in images of part of Puglia Region, in Italy

Nowadays, agriculture is facing significant challenges, including climate change. Precision agriculture might address these issues by optimizing resource use and promoting sustainability. In this work, a case study of tomato crop monitoring is presented, employing a large amount of gas sensor data collected over three years (2020–2022) to develop models for phenological phase classification. A k-NN classifier achieved accuracies above 99% across multiple train/test splits, with AUC, sensitivity, specificity, precision, and F1-score above 98%. Results demonstrate the feasibility of low-computational-cost systems capable of real-time detection of the transition point between plants’ developmental stages.

This study aims to utilize gaseous emissions from cultivation to predict the crop coefficient, an indirect measure of plant water stress. An array of chemiresistive gas sensors was used to collect the data, which were then processed by a simple artificial neural network algorithm. The results highlighted that the gas emissions from tomato crops can be used as a reliable indicator for determining the crop coefficient, allowing for more efficient and effective irrigation management. This research offers a potential solution to the growing issue of water scarcity in agriculture by providing a cost-effective and practical method for monitoring crop water status.