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The Leaf Area Index (LAI) is an ecophysiology key parameter characterising the canopyatmosphere interface where most of the energy fluxes are exchanged. However, producing maps for managing the spatial and temporal variability of LAI in large croplands with traditional techniques is typically laborious and expensive. The objective of this paper is to evaluate the reliability of LAI estimation by processing dense 3D point clouds as a costeffective alternative to traditional LAI assessments. This would allow for high resolution, extensive and fast mapping of the index, even in hilly and not easily accessible regions. In this setting, the 3D point clouds were generated from UAV-based multispectral imagery and processed by using an innovative methodology presented here. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for canopy thickness, height and leaf density distribution along the wall. For the validation of the estimated LAI, an experiment was conducted in a vineyard in Piedmont: the leaf area of 704 vines was manually measured by the inclined point quadrant approach and six UAV flights were contextually performed to acquire the aerial images. The vineyard LAI estimated by the proposed methodology showed to be correlated with the ones obtained by the traditional manual method. Indeed, the obtained R 2 value of 0.82 can be considered fully adequate, compatible to the accuracy of the reference LAI manual measurement.

11 12In the food industry, radio-frequency identification systems could be exploited for 13 traceability, logistics as well as for anti-counterfeit purposes. In this paper, a complete item-14 level radio-frequency (RF) traceability system is presented for a high-value, pressed, long-15 ripened cheese. The main contribution of this paper consists in experimenting with different 16 techniques for fixing tags to the cheese and solutions for automatic identification adapted to 17 handling procedures as implemented in a dairy factory. All item movements are thus 18 automatically recorded during the production, handling in the maturing room and warehouse, 19 delivery, packing and selling phases. 20Fixed and mobile RF devices operating at low, high and ultra-high frequency bands were 21 considered for both static and dynamic identification of single/multiple cheese wheels. 22Factors such as tag type and shape, required power, antennas polarization and orientation, 23 fixing method and ripening duration were considered in order to verify their effect on 24 reading performance and system reliability. 25 26