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Abstract: Morphological assessment is one important parameter considered in conservation and improvement programs for bovine livestock. This assessment process consists of scoring an animal based on its morphology and is normally carried out by highly qualified staff. These animals are all of agreed ‘show quality’ and hence they are morphologically very similar. This paper presents a system designed to provide an assessment based on a lateral image of the cow. The system consists of two main parts: a feature extraction stage, to reduce the information on the cow in the image to a set of parameters, and a neural network stage to provide a score based on that set of parameters. For the image analysis section, a model of the animal is constructed by means of the point distribution model (PDM) technique. Later, that model is used in the searching process within each image, which is implemented using genetic algorithms (GAs). As a result of this stage, the vector of weights that describe the deviation of the given shape from the mean is obtained. This vector is used in the second stage, where a multilayer perceptron is trained to provide the desired assessment, using the scores given by experts for selected cows. The system has been tested with 138 images corresponding to 44 individuals of a special rustic breed, with very promising results, given that the information contained in only one view of the cow can not be considered complete. [Copyright &y& Elsevier]

• Locate and classify broccoli heads based to its state of maturity. • The proposed system can be adapted, being able to work directly with natural light. • The cost of the required computational equipment is quite low for agriculture applications. Broccoli is a vegetable grown worldwide due to its good nutritional properties. The harvest of this product is done selectively by hand depending on their size and state of maturation for both fresh market and agri-food industry. The final aim of our work is the development of a machine that is able to automatically harvest only those broccoli heads that have the size and ripeness suitable for the agri-food industry, besides discarding those overripe or with diseases. One critical element in such a machine is a vision system that locates and classifies the broccoli heads present in photographic images, to trigger later a cutting module. In this paper, we present an approach to that vision system, based on deep learning techniques. The proposed algorithm, running in a relatively cheap hardware, is able to work in real time, locating broccoli heads in 640 × 480 px digital images, and classifying then into harvestable , immature and wasted classes. Tested with images taken in real conditions, with many heads partially hidden by leaves, the system was able to correctly locate and classify up to 97% of the cases presented in the test set. [ABSTRACT FROM AUTHOR]