The Association between Total Mixed Ration Particle Size and Fecal Scores in Holstein Lactating Dairy Cows from Florida, USA

Meléndez, P.; Roy, Elizabeth

doi:10.3844/ajavsp.2016.33.40

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Results were different compared with data reported by Melendez and Roy (2016) who found greater frequency of score 2.5 in Holstein lactating cows in Florida. Figure 3 shows the results obtained with a one-step and a two-steps training strategy.…”

Section: Resultscontrasting

confidence: 99%

An innovative technique for faecal score classification based on RGB images and artificial intelligence algorithms

Ortenzi¹,

Violino²,

Costa³

et al. 2023

J. Agric. Sci.

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The milk production is strongly influenced by the dairy cow welfare related to a good nutrition and the analysis of the digestibility of feeds allows us to evaluate the health status of the animals. Through faeces’ visual examination it is possible to estimate the quality of diet fed in terms of lacking in fibre or too high in non-structural carbohydrates. The study was carried out in 2021, on four dairy farms in central Italy. The purpose of this work is the classification and evaluation of dairy cow faeces using RGB image analysis through an artificial intelligence (AI) (convolutional neural network (CNN)) algorithm. The main features to analyse are pH, colour and consistency. For the latter two RGB imaging was combined with deep learning and AI to reach objectivity in samples’ evaluation. The images have been captured with several smartphones and cameras, under various light conditions, collecting a data set of 441 images. Images acquired by RGB cameras are then analysed through CNN technology that extracts features and data previously standardized by a faecal score index assigned after a visual analysis and based on five classes. The results achieved with different training strategies show a training accuracy of 90% and a validation accuracy of 78% of the model which allow us to identify problems in bovine digestion and to intervene promptly in feed variation. The method used in this study eliminates subjectivity in field analysis and allows future improvement of increasing the data set to strengthen the model.

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Section: Resultscontrasting

confidence: 99%

An innovative technique for faecal score classification based on RGB images and artificial intelligence algorithms

Ortenzi¹,

Violino²,

Costa³

et al. 2023

J. Agric. Sci.

View full text Add to dashboard Cite

show abstract

“…This paper proposes a method to parametrise faecal consistency as an indicator of gut health and diet fibre content using computer vision. This is a less subjective version of current on-farm visual assessment scores of faecal consistency 6 – 8 . A portable imaging system has been developed to include near-infrared (NIR) or visible light sources able to capture and analyse the three-dimensional appearance of samples and formulate an objective health score based on consistency.…”

Section: Introductionmentioning

confidence: 99%

A computer vision approach to improving cattle digestive health by the monitoring of faecal samples

Atkinson

Smith

et al. 2020

Sci Rep

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The digestive health of cows is one of the primary factors that determine their well-being and productivity. Under- and over-feeding are both commonplace in the beef and dairy industry; leading to welfare issues, negative environmental impacts, and economic losses. Unfortunately, digestive health is difficult for farmers to routinely monitor in large farms due to many factors including the need to transport faecal samples to a laboratory for compositional analysis. This paper describes a novel means for monitoring digestive health via a low-cost and easy to use imaging device based on computer vision. The method involves the rapid capture of multiple visible and near-infrared images of faecal samples. A novel three-dimensional analysis algorithm is then applied to objectively score the condition of the sample based on its geometrical features. While there is no universal ground truth for comparison of results, the order of scores matched a qualitative human prediction very closely. The algorithm is also able to detect the presence of undigested fibres and corn kernels using a deep learning approach. Detection rates for corn and fibre in image regions were of the order 90%. These results indicate the potential to develop this system for on-farm, real time monitoring of the digestive health of individual animals, allowing early intervention to effectively adjust feeding strategy.

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The effects of ration particle size and live yeast supplementation on dairy cows performance under heat stress conditions

Cengiz

2023

Trop Anim Health Prod

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The Association between Total Mixed Ration Particle Size and Fecal Scores in Holstein Lactating Dairy Cows from Florida, USA

Cited by 5 publications

References 24 publications

An innovative technique for faecal score classification based on RGB images and artificial intelligence algorithms

An innovative technique for faecal score classification based on RGB images and artificial intelligence algorithms

A computer vision approach to improving cattle digestive health by the monitoring of faecal samples

The effects of ration particle size and live yeast supplementation on dairy cows performance under heat stress conditions

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