The use of wearable sensors that record animal activity in intensive livestock systems has become more and more frequent for both early detection of diseases and improving quality of production. Their application may be also significant in extensive livestock systems, where there is an infrequent farmer-to-animal contact. The aim of the present study was to prove the feasibility of a novel automatic system for locating and tracking cows in extensive livestock systems based on space-time data provided by a low power global positioning system (LP-GPS). The information was used to study the pasture exploitation by the herd for modelling the environmental impacts of extensive livestock systems, trough Geographical Information Systems. A customized device, placed within a rectangular PVC case compatible with the collar usually worn by animals, was equipped with a LP-GPS omnidirectional system, an integrated SigFox communication system and a power supply. The experimental trial was carried out in an existing semi-natural pasture characterized by good pasture allowance and cultivated grazing areas. Ten cows were embedded with LP-GPS collars and the data, i.e., geographical coordinates and the time intervals related to each cow detection, were recorded every 20 minutes. Data were collected through a specifically developed AppWeb to be further imported and elaborated by using a GIS software tool. In GIS environment, the daily distances travelled by each cow were linked with Heatmaps obtained by applying Kernel Density Estimation models from the points obtained from the LP-GPS collars. The results of the study made it possible to obtain information on some relevant aspects for livestock’s environmental issues. In detail, it was possible to acquire information on herd behaviour related to the use of the pasture, e.g., the area of the pasture most frequently used during the day, individual use of the pasture, possible animal interactions. These results represent a first step towards further insights and research activities because monitoring of animal locations could allow the reduction of several environmental issues such as soil degradation and greenhouses emissions.
Animal welfare is becoming an increasingly important requirement in the livestock sector to improve, and therefore raise, the quality and healthiness of food production. By monitoring the behaviour of the animals, such as feeding, rumination, walking, and lying, it is possible to understand their physical and psychological status. Precision Livestock Farming (PLF) tools offer a good solution to assist the farmer in managing the herd, overcoming the limits of human control, and to react early in the case of animal health issues. The purpose of this review is to highlight a key concern that occurs in the design and validation of IoT-based systems created for monitoring grazing cows in extensive agricultural systems, since they have many more, and more complicated, problems than indoor farms. In this context, the most common concerns are related to the battery life of the devices, the sampling frequency to be used for data collection, the need for adequate service connection coverage and transmission range, the computational site, and the performance of the algorithm embedded in IoT-systems in terms of computational cost.
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<p>The significant efforts of the last years in new monitoring techniques and networks have led to large datasets and improved our capabilities to measure volcano conditions. &#160;Thus nowadays the challenge is to retrieve information from this huge amount of data to significantly improve our capability to automatically recognize signs of potentially hazardous unrest.<br>Unrest detection from unlabeled data is a particularly challenging task, since the lack of annotations on the temporal localization of these phenomena makes it impossible to train a machine learning model in a supervised way. The proposed approach, therefore, aims at learning unsupervised low-dimensional representations of the input signal during normal volcanic activity by training a variational autoencoder (VAE) to compress, reconstruct and synthesize input signals. Thanks to the internal structure of the proposed VAE architecture, with 1-dimensional convolutional layers with residual blocks and attention mechanism, the representation learned by the model can be employed to detect deviations from normal volcanic activity. In our experiments, we test and evaluate two techniques for unrest detection: a generative approach, with a bank of synthetic signals used to assess the degree of correspondence between normal activity and an input signal; and a discriminative approach, employing unsupervised clustering in the VAE representation space to identify prototypes of normal activity for comparison with an input signal.</p>
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