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The general aim of the project SUS&LOW is to increase the sustainability of fresh produce by testing and implementing lowinput agricultural practices (LIP) with positive impact on product quality with the support of nondestructive (ND) tools for realtime quality assessment and for product discrimination. Additionally, new marketing strategies are generated to better support the added value of the products and to satisfy the final consumers' preferences. The SUS&LOW project consists of three work packages (WP) and the adopted methodology used two model crops: rocket salad and tomato. The WP1, focused on the reduction of agricultural inputs, showed that sensorbased fertigation management might improve sustainability of soilless cultivation. Results coming from WP2, aimed to the evaluation of ND techniques, outlined the high potentiality of hyperspectral imaging (HSI) and Fourier transformednear infrared (FTNIR) techniques for the authentication of sustainable growing methods. Moreover, project activities' proved computer vision system (CVS) as an effective tool for evaluating the product quality also through the bag. The WP3, dealing with marketing strategies, indicated a positive approach of consumers compared to LIP products certified through a visual storytelling platform. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to describe a neural algorithm for classification and segmentation of vector flow fields. We propose to use the coefficients of their projection into an appropriate linear space as a feature vector for classification. The projection onto a suitable set of basis vectors is computed by satisfying global optimization criteria. Once the whole flow field is partitioned into a large number of small patches, two processes are performed. In the former, each small patch is classified using the associated projection coefficients estimated by using a least-square-error (LSE) technique implemented on a neural network. In the latter, segmentation into larger homogeneous regions is performed using a region growing method. Two application contexts are considered: analysis of oriented textures and 3D motion. The Lie group theory is used to identify the basis vectors suitable for defining the vector space describing the patterns of interest. In particular, the projection onto the image plane of the 3D infinitesimal generators of the 3D Euclidean group have proved to provide an effective description for the considered vector flow fields. [ABSTRACT FROM AUTHOR]

In this work we consider the application context of planar passive navigation in which the visual control of locomotion requires only the direction of translation, and not the full set of motion parameters. If the temporally changing optic array is represented as a vector field of optical velocities, the vectors form a radial pattern emanating from a centre point, called the Focus of Expansion (FOE), representing the heading direction. The FOE position is independent of the distances of world surfaces, and does not require assumptions about surface shape and smoothness. We investigate the performance of an artificial neural network for the computation of the image position of the FOE of an Optical Flow (OF) field induced by an observer translation relative to a static environment. The network is characterized by a feed-forward architecture, and is trained by a standard supervised back-propagation algorithm which receives as input the pattern of points where the lines generated by 2D vectors are projected using the Hough transform. We present results obtained on a test set of synthetic noisy optical flows and on optical flows computed from real image sequences. [ABSTRACT FROM AUTHOR]