Análise de imagens para a caracterização das atividades de vacas leiteiras dentro do galpão de confinamento

Souza, Silvia Regina Lucas de; Nääs, Irenilza de Alencar; Moura, Daniella Jorge de

doi:10.1590/s0100-69162011000600001

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…The analysis presents R² coefficients of 66.68%, which means an adjustment of 66.68% of the automated method to the visual method. According to studies there is a high standard deviation between image analysis methods, and there is an indication of possible causes, such as the grouping of animals forming a single mass in the image and the spots on the coat that can confuse the image analyzed by the algorithm [ 30 ].…”

Section: Resultsmentioning

confidence: 99%

Thermal Environment and Behavior Analysis of Confined Cows in a Compost Barn

Laurindo

Ferraz

Damasceno

et al. 2022

Animals

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The compost barn system has become popular in recent years for providing greater animal well-being and quality of life, favoring productivity and longevity. With the increase in the use of compost barn in dairy farms, studies related to the thermal environment and behavior are of paramount importance to assess the well-being of animals and improve management, if necessary. This work aimed to characterize the thermal environment inside a compost barn during the four seasons of a year and to evaluate the standing and lying behavior of the cows through images. The experiment was carried out during March (summer), June (autumn), August (winter), and November (spring). Dry bulb temperature (tdb, oC), dew point temperature (tdp, oC), and relative humidity (RH, %) data were collected every 10 minutes during all analyzed days, and the temperature and humidity index (THI) was subsequently calculated. In order to analyze the behavior of the cows, filming of the barn interior was carried out during the evaluated days. Subsequently, these films were analyzed visually, and in an automated way to evaluate the behavior of these animals. For the automated analysis, an algorithm was developed using artificial intelligence tools, YOLOv3, so that the evaluation process could be automated and fast. It was observed that during the experimental period, the highest mean values of THI were observed during the afternoon and the autumn. The animals' preference to lie down on the bed for most of the day was verified. It was observed that the algorithm was able to detect cow behavior (lying down or standing). It can be concluded that the behavior of the cows was defined, and the artificial intelligence was successfully applied and can be recommended for such use.

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Section: Resultsmentioning

confidence: 99%

Thermal Environment and Behavior Analysis of Confined Cows in a Compost Barn

Laurindo

Ferraz

Damasceno

et al. 2022

Animals

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“…Using this methodology, the outward behavior of animals is not influenced by the human presence, in addition to facilitating the search for more specific and accurate observations in animal behavior. Such behavior can be analyzed repeatedly when it is necessary to capture images of more relevant conducts (Silva et al, 2006;Barbosa Filho et al, 2007;Cordeiro et al, 2011;Souza et al, 2011). The automatic analysis of digital images from video recordings represents an effective alternative to traditional techniques used in behavior studies (Provolo and Riva, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Many studies on the behavior of production animals using video images (mainly cattle, birds and pigs) are found in the national or international literature (Provolo and Riva, 2009;Porto et al, 2011;Souza et al, 2011;Nääs et al, 2012;Costa et al, 2013), but not for Brazilian native goats, which reinforces the need for studies using this approach on these animals. In this sense, considering the lack of studies on the behavior of confined native goats and the emergence of image monitoring as an important tool to assess animal behavior, this study aimed at evaluating the performance of Azul, Graúna and Moxotó goats kept in confinement by image monitoring.…”

Section: Introductionmentioning

confidence: 99%

Image monitoring on the behavior study of three genetic groups of confined goats

et al. 2014

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-The objective of this study was to analyze the behavior of three genetic groups of confined goats by image monitoring using nine unneutered goats of the Azul, Graúna and Moxotó genotypes. The experimental design was completely randomized, in a 3 × 3 factorial arrangement (genetic groups × periods -morning, afternoon and night), with three replications. Animals were monitored by video cameras placed at the ceiling of each stall for 10 days. There were interaction factors in the frequency of behavioral categories: interaction with the trough, self-cleaning, standing bipedally, social interaction, and other activities. Azul, Graúna and Moxotó native goats kept in the same environment under the same system of containment and feeding show different frequencies and durations in their behaviors, which are influenced by the period of the day.

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“…Machine vision systems have been reported to analyse confined dairy cattle behaviour (SOUZA et al, 2011) and also the use of non-classic logic tools to evaluate livestock slaughtering is found in current literature (GABRIEL FILHO et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Preventive diagnosis of dairy cow lameness

Neto¹,

Nääs²,

Carvalho³

et al. 2014

Eng. Agríc.

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This research aimed to develop a Fuzzy inference based on expert system to help preventing lameness in dairy cattle. Hoof length, nutritional parameters and floor material properties (roughness) were used to build the Fuzzy inference system. The expert system architecture was defined using Unified Modelling Language (UML). Data were collected in a commercial dairy herd using two different subgroups (H 1 and H 2 ), in order to validate the Fuzzy inference functions. The numbers of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) responses were used to build the classifier system up, after an established gold standard comparison. A Lesion Incidence Possibility (LIP) developed function indicates the chances of a cow becoming lame. The obtained lameness percentage in H 1 and H 2 was 8.40% and 1.77%, respectively. The system estimated a Lesion Incidence Possibility (LIP) of 5.00% and 2.00% in H 1 and H 2 , respectively. The system simulation presented 3.40% difference from real cattle lameness data for H 1 , while for H 2 , it was 0.23%; indicating the system efficiency in decision-making.

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Análise de imagens para a caracterização das atividades de vacas leiteiras dentro do galpão de confinamento

Cited by 4 publications

References 13 publications

Thermal Environment and Behavior Analysis of Confined Cows in a Compost Barn

Thermal Environment and Behavior Analysis of Confined Cows in a Compost Barn

Image monitoring on the behavior study of three genetic groups of confined goats

Preventive diagnosis of dairy cow lameness

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