Towards combining data prediction and internet of things to manage milk production on dairy cows

Righi, Rodrigo da Rosa; Goldschmidt, Guilherme; Kunst, Rafael; Deon, Cássio; Costa, Cristiano André da

doi:10.1016/j.compag.2019.105156

Cited by 41 publications

(9 citation statements)

References 42 publications

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“…Animal well-being, issues related to the principle of species-appropriate housing; In the case of livestock, the Internet of Things may promote, e.g., an eco-efficient cattle management [57,58]. Battery-powered sensors on collars can monitor livestock location and crucial physiological parameters (temperature, blood pressure, heart rate) opening to the possibility of differentiated feeding.…”

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confidence: 99%

The Digitalization of Agriculture and Rural Areas: Towards a Taxonomy of the Impacts

et al. 2021

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The literature about digitalization in agriculture and rural areas is vast and sectorial at the same time. Both international political institutions and practitioners are interested in promoting digital technology, indicating and describing potential benefits and risks. Meanwhile, academics analyze the actual and possible impacts of digital technologies by using case studies. However, the extensive literature makes it challenging to derive a comprehensive synthesis of the possible impacts that digital technologies are and might generate in the rural domains. In the given context, the present work aims at contributing to the construction of a framework providing a first classification of the digital technologies’ impacts to use in both research and a political agenda.

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confidence: 99%

The Digitalization of Agriculture and Rural Areas: Towards a Taxonomy of the Impacts

et al. 2021

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“…There are several reasons for this higher consideration. Firstly, scientific knowledge has increased, demonstrating the need to make cows live in a safe and healthy environment to improve their welfare conditions and the related milk production (2,3). Secondly, the market and the consumers have been affecting production patterns because of their increased consciousness about the living conditions of animals (4).…”

Section: Introductionmentioning

confidence: 99%

“…Finally, technological progress is giving the possibility to stakeholders, mainly farmers, policymakers, and consumers, to obtain a big amount of data on production aspects and on the relation among the different parameters. This opportunity reflects on the farmers' capability to monitor continuously the reared animals, get advantages from the animal-by-animal control, and introduce prompt interventions when necessary (3,(5)(6)(7). Technology, wireless connectivity, sensors, and monitoring tools are adopted in this analysis framework, recognized as precision livestock farming (PLF).…”

Section: Introductionmentioning

confidence: 99%

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Relating Lying Behavior With Climate, Body Condition Score, and Milk Production in Dairy Cows

et al. 2020

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Attention on animal behavior and welfare has been increasing. Scientific knowledge about the effect of behavior and welfare on animals' production augmented and made clear the need of improving their living conditions. Among the variables to monitor in dairy cattle farming, lying time represents a signal for health and welfare status as well as for milk production. The aim of this study is to identify the relationship among the lying behavior of dairy cows and milk production, body condition score (BCS), weather variables, and the temperature-humidity index (THI) in the barn from a dairy farm located in Northern Italy. One-year data were collected on this farm with sensors that allowed monitoring of the environmental conditions in the barn and the activity of primiparous lactating cows. Principal components analysis (PCA), factor analysis (FA), generalized linear model select (GLMSelect), and logistic analysis (LA) were carried out to get the relationships among variables. Among the main results, it emerges that the effect of weather parameters is quite restrained, except for THI > 70, which negatively affects the lying time. In addition, the most productive cows are found to lie down more than the less productive ones, and the parameters of milk production, lying time, and BCS are found to be linked by a similar trend.

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“…Ref. [ 45 ] provided forecasts of individual milk production via time series ARIMA models. The k nearest neighbour (kNN) principle was introduced by [ 46 ] and is based on the idea that samples of the same class are closed to each other in the feature space.…”

Section: Introductionmentioning

confidence: 99%

Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

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Haas

Junge

et al. 2020

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Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application.

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Towards combining data prediction and internet of things to manage milk production on dairy cows

Cited by 41 publications

References 42 publications

The Digitalization of Agriculture and Rural Areas: Towards a Taxonomy of the Impacts

The Digitalization of Agriculture and Rural Areas: Towards a Taxonomy of the Impacts

Relating Lying Behavior With Climate, Body Condition Score, and Milk Production in Dairy Cows

Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

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