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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]

In the present work, a neural-networks-based system is presented that makes it possible to reduce, when generating edge maps to be used in an object boundary detection problem, the number of edges that are not due to the object itself, but to the background. Starting from a conventional edge detection, the selection is carried out by a neural-networks-based classifier, which is trained using examples. As a test for the system, the application to bovine livestock images (from which we want to extract the boundary of the animal) is presented. [Copyright &y& Elsevier]

In this work, we present a complete hardware development and current consumption study of a portable electronic nose designed for the Internet-of-Things (IoT). Thanks to the technique of measuring in the initial action period, it can be reliably powered with a moderate-sized battery. The system is built around the well-known SoC (System on Chip) ESP8266EX, using low-cost electronics and standard sensors from Figaro's TGS26xx series. This SoC, in addition to a powerful microcontroller, provides Wi-Fi connectivity, making it very suitable for IoT applications. The system also includes a precision analog-to-digital converter for the measurements and a charging module for the lithium battery. During its operation, the designed software takes measurements periodically, and keeps the microcontroller in deep-sleep state most of the time, storing several measurements before uploading them to the cloud. In the experiments and tests carried out, we have focused our work on the measurement and optimization of current consumption, with the aim of extending the battery life. The results show that taking measurements every 4 min and uploading data every five measurements, the battery of 750 mAh needs to be charged approximately once a month. Despite the fact that we have used a specific model of gas sensor, this methodology is quite generic and could be extended to other sensors with lower consumption, increasing very significantly the duration of the battery. [ABSTRACT FROM AUTHOR]

• 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]

The determination of daily concentrations of atmospheric pollen is important in the medical and biological fields. Obtaining pollen concentrations is a complex and time-consuming task for specialized personnel. The automatic location of pollen grains is a handicap due to the high complexity of the images to be processed, with polymorphic and clumped pollen grains, dust, or debris. The purpose of this study is to analyze the feasibility of implementing a reliable pollen grain detection system based on a convolutional neural network architecture, which will be used later as a critical part of an automated pollen concentration estimation system. We used a training set of 251 videos to train our system. As the videos record the process of focusing the samples, this system makes use of the 3D information presented by several focal planes. Besides, a separate set of 135 videos (containing 1234 pollen grains of 11 pollen types) was used to evaluate detection performance. The results are promising in detection (98.54% of recall and 99.75% of precision) and location accuracy (0.89 IoU as the average value). These results suggest that this technique can provide a reliable basis for the development of an automated pollen counting system. [ABSTRACT FROM AUTHOR]