BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture1

@article{Morota2018BIGDA,
  title={BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture1},
  author={G. Morota and Ricardo Vieira Ventura and Fabyano Fonseca e Silva and Masanori Koyama and Samodha Charaka Fernando},
  journal={Journal of Animal Science},
  year={2018},
  volume={96},
  pages={1540 - 1550}
}
Abstract Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the use of tools to routinely monitor and collect information from animals and farms in a less laborious manner than before. These efforts have enabled the animal sciences to embark on information technology-driven discoveries to improve animal… 

A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock

TLDR
The opportunities and challenges in using high-throughput phenotyping, “big data,” analytics, and related technologies in the livestock industry are described based on discussions at the Livestock High-Throughput Phenotyping and Big Data Analytics meeting, held in November 2017.

Predictive analytics using cross media features in precision farming

TLDR
This manuscript is reviewing the existing set of computer aided methods of predictive analytics defined in related to precision farming, gaining insights into how distinct set of precision farming inputs are supporting the predictive analytics to help farming communities towards improvisation.

Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

  • M. Cockburn
  • Computer Science, Medicine
    Animals : an open access journal from MDPI
  • 2020
TLDR
It was found that ML methods were applied to predict data in a variety of areas in dairy farming such as milk yield or energy consumption; however, larger integrated datasets are required to improve the reliability of the algorithms developed.

Ethics of Using AI and Big Data in Agriculture: The Case of a Large Agriculture Multinational

Smart information systems (Big Data and artificial intelligence) are used in the agricultural industry to help the planting, seeding, and harvesting of crops, as well as farm management, plant and

Symposium review: Dairy Brain-Informing decisions on dairy farms using data analytics.

TLDR
A decision support tool that couples data analytics tools to underlying cow, herd, and economic data with an application programming interface is described that allows the user to interact with a collection of dairy applications without fully exposing the intricacies of the underlying system model.

Forecasting beef production and quality using large scale integrated data from Brazil.

TLDR
By integrating different sources of data it is possible to forecast meat production and quality at the national level with moderate-high levels of accuracy, and across all models there was a tendency for better performance with RF and regression and worse with NN.

An Implementation of IoT and Data Analytics in Smart Agricultural System – A Systematic Literature Review

  • K. VikranthK. Prasad
  • Computer Science
    International Journal of Management, Technology, and Social Sciences
  • 2021
TLDR
Big data's role in Agriculture affords prospect to increase the farmers' economic gain by undergoing a digital revolution in this aspect that is examined with precision.

The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence

Abstract Livestock welfare assessment helps monitor animal health status to maintain productivity, identify injuries and stress, and avoid deterioration. It has also become an important marketing

Review: Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data.

TLDR
A cross-species examination of the current state of the art in MM and new DD methodologies is undertaken to propose means to hybridize these two seemingly divergent methodologies to advance the models used in animal production systems and support movement towards truly knowledge-based precision agriculture.

References

SHOWING 1-10 OF 62 REFERENCES

On-line detection of mastitis in dairy herds using artificial neural networks

The data include milking data collected over a four month time period by robots on four farms and monthly test-day milk data collected by veterinarian for determination of the incidence of clinical

Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers.

In genome-wide association studies using single nucleotide polymorphisms (SNPs), typically thousands of SNPs are genotyped, whereas the number of phenotypes for which there is genomic information may

Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators

TLDR
The most pressing unmet needs are training in data integration, data management, and scaling analyses for HPC—acknowledging that data science skills will be required to build a deeper understanding of life.

Kernel-based whole-genome prediction of complex traits: a review

TLDR
This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information.

Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants

TLDR
The discovery that rumen microbiome components are tightly linked to cows' ability to extract energy from their feed, termed feed efficiency, is reported.

Toward a Predictive Understanding of Earth’s Microbiomes to Address 21st Century Challenges

TLDR
This work outlines strategies to move microbiome research into an era of causality, and proposes that a coordinated, cross-disciplinary effort is required to understand, predict, and harness microbiome function.

Metagenomic Predictions: From Microbiome to Complex Health and Environmental Phenotypes in Humans and Cattle

TLDR
Preliminary results suggest this approach may surpass current prediction accuracies that are based on the host genome, especially likely for traits that are largely influenced by the gut microbiota, for example digestive tract disorders or metabolic functions such as enteric methane production in cattle.

Technical note: Estimating body weight and body composition of beef cattle trough digital image analysis.

TLDR
This study indicates that digital images taken through a Microsoft Kinect system have the potential to be used as a tool to estimate body and carcass weight of beef cattle.
...