Carpentier, Lenn
Berckmans, Daniel
Youssef, Ali
Berckmans, Dries
van Waterschoot, Toon
Johnston, Dayle
Ferguson, Natasha
Earley, Bernadette
Fontana, Ilaria
Tullo, Emanuela
Guarino, Marcella
Vranken, Erik
Norton, Tomas
In calf rearing, bovine respiratory disease (BRD) is a major animal health challenge. Farmers incur severe economic losses due to BRD. Additional to economic costs, outbreaks of BRD impair the welfare of the animal and extra expertise and labour are needed to treat and care for the infected animals. Coughing is recognised as a clinical manifestation of BRD. Therefore, the monitoring of coughing in a calf house has the potential to detect cases of respiratory infection before they become too severe, and thus to limit the impact of BRD on both the farmer and the animal. The objective of this study was to develop an algorithm for detection of coughing sounds in a calf house. Sounds were recorded in four adjacent compartments of one calf house over two time periods (82 and 96 days). There were approximately 21 and 14 calves in each compartment over the two time-periods, respectively. The algorithm was developed using 445 min of sound data. These data contained 664 different cough references, which were labelled by a human expert. It was found that, during the first time period in all 3 of the compartments and during the second period in 2 out of 4 compartments, the algorithm worked very well (precision higher than 80%), while in the 2 other cases the algorithm worked well but the precision was less (66.6% and 53.8%). A relation between the number of calves diagnosed with BRD and the detected coughs is shown. (C) 2018 lAgrE. Published by Elsevier Ltd. All rights reserved.
Fontana, Ilaria
Tullo, Emanuela
Carpentier, Lenn
Berckmans, Dries
Butterworth, Andy
Vranken, Erik
Norton, Tomas
Berckmans, Daniel
Guarino, Marcella
The pattern of body weight gain during the commercial growing of broiler chickens is important to understand growth and feed conversion ratio of each flock. The application of sound analysis techniques has been widely studied to measure and analyze the amplitude and frequency of animal sounds. Previous studies have shown a significant correlation (P <=3D 0.001) between the frequency of vocalization and the age and weight of broilers. Therefore, the aim of this study was to identify and validate a model that describes the growth rate of broiler chickens based on the peak frequency of their vocalizations and to explore the possibility to develop a tool capable of automatically detecting the growth of the chickens based on the frequency of their vocalizations during the production cycle. It is part of an overall goal to develop a Precision Livestock Farming tool that assists farmers in monitoring the growth of broiler chickens during the production cycle. In the present study, sounds and body weight were continuously recorded in an intensive broiler farm during 5 production cycles. For each cycle the peak frequencies of the chicken vocalizations were used to estimate the weight and then they were compared with the observed weight of the birds automatically measured using on farm automated weighing devices. No significant difference is shown between expected and observed weights along the entire production cycles; this trend was confirmed by the correlation coefficient between expected and observed weights (r =3D 96%, P value =3D 0.001). The identified model used to predict the weight as a function of the peak frequency confirmed that bird weight might be predicted by the frequency analysis of the sounds emitted at farm level. Even if the precision of the weighing method based on sounds investigated in this study has to be improved, it gives a reasonable indication regarding the growth of broilers opening a new scenario in monitoring systems in broiler houses.
Lu, Mingzhou
He, Ju
Chen, Chao
Okinda, Cedric
Shen, Mingxia
Liu, Longshen
Yao, Wen
Norton, Tomas
Berckmans, Daniel
Ear bases are considered the thermal windows of a piglet. Temperature variation in piglet ear bases can be used as the indicator of a piglet's health status. However, piglet skin temperatures in thermal windows in the existing research are obtained manually from infrared thermal images captured by a thermography. This has put an obstacle at the automatic identification of piglets with health disorder. An algorithm was proposed in this paper to extract ear base temperature automatically from top view piglet thermal images. Firstly, a SVM (Support Vector Machine) classifier was trained to identify piglet head part. Then, two ear base points were located based on the shape feature of the head part contour. Finally, two maximum temperatures inside the two circles centered by ear base points were extracted as the ear base temperatures. The proposed algorithm was implemented in Matlab (R) (R2016a) and applied to 100 testing images. The extracted ear base temperatures were compared with those extracted manually by using Fluke SmartView 3.14 (FLUKE Systems). Comparison results showed that for left and right ear base respectively, 97% and 98% of the testing images had an error within 0.4 degrees C. Ear base temperatures with such accuracy provided a foundation for the automatic identification of sick piglets.
Adeyemi, Olutobi
Grove, Ivan
Peets, Sven
Domun, Yuvraj
Norton, Tomas
Sustainable freshwater management is underpinned by technologies which improve the efficiency of agricultural irrigation systems. Irrigation scheduling has the potential to incorporate real-time feedback from soil moisture and climatic sensors. However, for robust closed-loop decision support, models of the soil moisture dynamics are essential in order to predict crop water needs while adapting to external perturbation and disturbances. This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. The models are trained to generate a one-day-ahead prediction of the volumetric soil moisture content based on past soil moisture, precipitation, and climatic measurements. Using field data from three sites, a value above 0.94 was obtained during model evaluation in all sites. The models were also able to generate robust soil moisture predictions for independent sites which were not used in training the models. The application of the Dynamic Neural Network models in a predictive irrigation scheduling system was demonstrated using AQUACROP simulations of the potato-growing season. The predictive irrigation scheduling system was evaluated against a rule-based system that applies irrigation based on predefined thresholds. Results indicate that the predictive system achieves a water saving ranging between 20 and 46% while realizing a yield and water use efficiency similar to that of the rule-based system.
The design of thermal processes in the food industry has undergone great developments in the last two decades due to the availability of cheap computer power alongside advanced modelling techniques such as computational fluid dynamics (CFD). CFD uses numerical algorithms to solve the non-linear partial differential equations of fluid mechanics and heat transfer so that the complex mechanisms that govern many food-processing systems can be resolved. In thermal processing applications, CFD can be used to build three-dimensional models that are both spatially and temporally representative of a physical system to produce solutions with high levels of physical realism without the heavy costs associated with experimental analyses. Therefore, CFD is playing an ever growing role in the development of optimization of conventional as well as the development of new thermal processes in the food industry. This paper discusses the fundamental aspects involved in developing CFD solutions and forms a state-of-the-art review on various CFD applications in conventional as well as novel thermal processes. The challenges facing CFD modellers of thermal processes are also discussed. From this review it is evident that present-day CFD software, with its rich tapestries of mathematical physics, numerical methods and visualization techniques, is currently recognized as a formidable and pervasive technology which can permit comprehensive analyses of thermal processing.
Ellen, Esther
van der Sluis, Malou
Siegford, Janice
Guzhva, Oleksiy
Toscano, Michael
Bennewitz, Jörn
van der Zande, Lisette
van der Eijk, Jerine
de Haas, Elske
Norton, Tomas
Piette, Deborah
Tetens, Jens
de Klerk, Britt
Visser, Bram
Rodenburg, T.
In livestock housing permeable windbreak materials are regularly used at eave openings to reduce the adverse effects of windy outdoor conditions on the indoor environment. Two materials generally used for this application include space boarding, i.e. a traditional timber cladding system, and ventilated cladding, i.e. a profiled steel sheeting system with louvers cut into the protruding ribs. In this study computational fluid dynamics (CFD) models were developed to investigate the ventilation performance and thermal environment of naturally ventilated calf buildings with unrestricted, space boarding and ventilated cladding eave opening conditions. The effect of altering the eave opening height on both the indoor environment and ventilation characteristics of the building has also been investigated. It was found that ventilated cladding performed the best in terms of ventilation efficiency and thermal comfort during wind driven ventilation, as the high resistance of the opening condition prevented the short-circuiting of air from the windward to the leeward opening. For high porosity eave opening conditions, increasing the eave opening height was found to decrease the average air velocity at animal level due to a change in size and location of the primary air recirculation zone. It was also found that the resistance and the height of an eave opening determine whether or not the leeward eave opening acts as an air inlet. (C) 2009 Elsevier B.V. All rights reserved.
Saraz, Jairo Alexander Osorio
Tin=C3=B4co, Ilda de F=C3=A1tima Ferreira
Rocha, Keller Sullivan Olivera
Mendes, Luciano Barreto
Norton, Tomas
The understanding of concentration and emissions distribution of gases such as ammonia (NH3) in agricultural installations is of growing importance due to its effect on health and productivity of animals and workers. The objective of this study was to use validated Computational Fluid Dynamics (CFD) model as a tool to predict NH3 concentration distribution and mass fluxes in a non-insulated broiler chicken installation with natural ventilation, typically found in subtropical and tropical countries. Results from this study indicated that simulation with CFD can be used to predict NH3 concentration distribution and mass flux inside similar installations with incident winds from different directions of entrance at the lateral opening of the installation. The most direct application of the proposed model would be to help improving the existing buildings and also to help in the conception of new ones, and may also apply the model to help in the development of NH3 emission inventories.=09
High pressure processing is a food processing method which has shown great potential in the food industry. Similar to heat treatment, high pressure processing inactivates microorganisms, denatures proteins and extends the shelf life of food products. But in the meantime, unlike heat treatments, high pressure treatment can also maintain the quality of fresh foods, with little effects on flavour and nutritional value. Furthermore, the technique is independent of the size, shape or composition of products. In this paper, many aspects associated with applying high pressure as a processing method in the food industry are reviewed, including operating principles, effects on food quality and safety and most recent commercial and research applications. It is hoped that this review will promote more widespread applications of the technology to the food industry.