1,721,048 research outputs found
A calculation model for the energy performance assessment of fattening pig houses
No full textClimate control in animal houses is a relevant issue. In fact, it provides appropriate breeding conditions, but it requires a considerable amount of energy with respect to the overall process consumption. For this reason, studies aimed at its management and optimization are needed. In this work, the main features and the potentialities of a model for the computation of the energy use for climate control and indoor environmental condition of pig houses is presented. In particular, the case of fattening pig houses equipped with mechanical ventilation system is considered. The calculation model is based on a customization of the simple hourly method of ISO 13790 Standard and it determines thermal and electrical energy uses for heating and ventilation purposes, respectively. The hourly indoor environmental conditions provided by the model can be also used to evaluate the quality of the indoor environment of the pig house. This work is part of the EPAnHaus project, which aims at defining an energy performance certification of livestock houses through measurements and development of numerical simulation models
Cooperation of unmanned systems for agricultural applications: A case study in a vineyard
Full text linkFully-autonomous vehicles, both aerial and ground, could provide great benefits in the Agriculture 4.0 framework when operating within cooperative architectures, thanks to their ability to tackle difficult tasks, particularly within complex irregular and unstructured scenarios such as vineyards on sloped terrains. A decentralised multi-phase approach has been proposed as an alternative to more common cooperative schemes. When perennial crops are considered, it is advantageous to build a simplified geometrical (and georeferenced) crops model, which can be identified by using 3D point clouds acquired during a-priori explorative missions by unmanned aerial vehicles. This model can be used to plan the tasks to be performed within the crops by the in-field aerial and ground drones. In this companion paper, the proposed strategy is applied to a specific case study involving a vineyard on a sloped terrain, located in the Barolo region in Piedmont, Italy. Ad-hoc technologies and guidance, navigation and control algorithms were designed and implemented. The main objectives were to improve the autonomous driving capabilities of the drones involved and to automate the process of retrieving low-complexity maps from the data collected with preliminary remote sensing missions to make them available for the autonomous navigation by a quadrotor and an unmanned 4-wheel steering ground vehicle within the vine rows. Preliminary results highlight the benefits achievable by exploiting the tailored technologies selected and applied to improve each of the analysed mission phases
L’efficientamento energetico in porcilaia
No full textLa simulazione energetica rappresenta uno strumento utile alla progettazione efficiente delle porcilaie, in quanto permette di effettuare analisi affidabili per quanto riguarda i consumi e le condizioni
ambientali
Concerning the Relationship Between Tilled Soil Aggregates Dimension and Power Harrow Energy Requirements
No full textThe literature reports energy requirements for several instruments based on different operating conditions and soil properties. However, tillage cannot be evaluated only by its energy consumption alone. Improvements in soil structure and consequent agricultural benefits must also be examined. The number of tine revolutions per meter (called C) is controlled by both the angular speed of the tine rotors and the machine's ground speed, which together allow power harrows to alter the soil's cloddiness. This study seeks to identify relationships between the seedbed quality and the energy needed to operate a power harrow in various configurations. A tractor with a 107 kW rated engine power and a power harrow with a 3 m operating width were tested in the field at the University of Bologna's experimental farm. Tractor metrics such as speed, engine power, fuel rate consumption, draught, and power take-off (PTO) speed and torque were recorded with a datalogger. Field tests were conducted by adjusting C from 2.3 rev rev m−1 to 12.57 rev m−1, following harrowing soil samples were sieved, and important granulometric parameters were computed and correlated with information obtained from the tractor-power harrow system. The findings indicate that high values of implement-soil impact speed are necessary to achieve the best seedbed conditions
Methods for traceability in food production processes involving bulk products
Full text linkIn food processing plants, raw materials are fed into the system in different supply-lots of product, and are processed through different stages. In these stages, raw or intermediate materials are mixed or combined together, and physico-chemical and/or microbiological processes such as heating, concentration, pasteurisation etc. take place. In this setting, traceability consists of the ability to determine for each portion of intermediate or final product, in any part of the plant, its relative composition in terms of supply-lots fed into the system as well as of new lots generated during the production process. Traceability becomes particularly difficult in the very common case when bulk products, such as liquids or grains, are involved in the production chain. Current traceability practices are in most cases unable to directly deal with bulk products, and typically resort to the definition of very large lots to compensate the lack of knowledge about lot composition. As demonstrated in recent food crises, this over-bounding approach has weaknesses in clearly identifying, immediately after risk assessment, the affected product lots, leading to unavoidably wide, expensive and highly impacting recalls. Motivated by these considerations, this paper presents a novel approach to manage traceability of bulk products during production, storage and delivery. It provides a tight definition of lots in terms of their composition and size, thus allowing strict control of the production and supply chain
UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture
Full text linkPrecision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite’s output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d’Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers
Unsupervised detection of vineyards by 3D point-cloud UAV photogrammetry for precision agriculture
Full text linkAn effective management of precision viticulture processes relies on robust crop monitoring procedures and, in the near future, to autonomous machine for automatic site-specific crop managing. In this context, the exact detection of vineyards from 3D point-cloud maps, generated from unmanned aerial vehicles (UAV) multispectral imagery, will play a crucial role, e.g. both for achieve enhanced remotely sensed data and to manage path and operation of unmanned vehicles.
In this paper, an innovative unsupervised algorithm for vineyard detection and vine-rows features evaluation, based on 3D point-cloud maps processing, is presented. The main results are the automatic detection of the vineyards and the local evaluation of vine rows orientation and of inter-rows spacing.
The overall point-cloud processing algorithm can be divided into three mains steps: (1) precise local terrain surface and height evaluation of each point of the cloud, (2) point-cloud scouting and scoring procedure on the basis of a new vineyard likelihood measure, and, finally, (3) detection of vineyard areas and local features evaluation.
The algorithm was found to be efficient and robust: reliable results were obtained even in the presence of dense inter-row grassing, many missing plants and steep terrain slopes. Performances of the algorithm were evaluated on vineyard maps at different phenological phase and growth stages. The effectiveness of the developed algorithm does not rely on the presence of rectilinear vine rows, being also able to detect vineyards with curvilinear vine row layouts
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