The role of interoperable data standards in precision livestock farming in extensive livestock systems: A review

Bahlo, Christiane; Dahlhaus, Peter; Thompson, Helen; Trotter, Mark

doi:10.1016/j.compag.2018.12.007

Cited by 96 publications

(68 citation statements)

References 81 publications

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“…Firstly, researchers are encouraged to search thoroughly to find previous work done in relation to their question of interest to avoid unintentional repetition, and instead use knowledge of what has previously been done to build on the successes and failures of others. The increasing number of recent reviews on aspects of PLF [143–146] can play a major contribution here. Second, improving the reporting standards in scientific publications related to using machine vision for automated detection of behaviour will make articles more useful and informative so other researchers can and will use them as they progress in their own work.…”

Section: Resultsmentioning

confidence: 99%

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

et al. 2019

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Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced.

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Section: Resultsmentioning

confidence: 99%

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

et al. 2019

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“…The use of adoptable technology following PLF principles is indeed one of the most investigated aspects for current and near future livestock farming. PLF is a solution that involves an initial economic investment but that has undeniable positive effects on the herd management and on guaranteeing environmental, economic and social sustainable livestock productions [24,32,[37][38][39]. Hence, its spread is increasing as it responds to the main requirements of new generations of farmers, consumers and policy makers that look for compromises for having efficient animal productions with satisfactory animal welfare, reduced environmental impact [40][41][42], low inputs purchase/expenses and low wear of resources [5,43].…”

Section: Discussionmentioning

confidence: 99%

A Survey of Dairy Cattle Behavior in Different Barns in Northern Italy

Lovarelli

Mattachini

et al. 2020

Animals

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Due to its increasing pressure on dairy cows, studies that investigate how to cope with heat stress are needed. The heat stress affects multiple aspects of cows’ lives, among which their behavior and welfare. In this study, a survey was carried out in eight farms located in Northern Italy to monitor and evaluate the environmental aspects of the barns and the behavioral responses of dairy cows. For one year, three periods were monitored: thermoneutral (T_S), hot (H_S) and cold (C_S) seasons. Temperature and relative humidity were measured by environmental sensors, and lying vs. standing time, number of lying bouts and their average duration were collected by accelerometers. The temperature-humidity index (THI) was quantified inside and outside of the barn. Results show that at the increase of the THI, behavioral adaptations occurred in all the farms, especially with a reduction of lying time and an increase of respiration rate. Four of the eight farms need interventions for improving the cows’ welfare. Here, environmental problems should be solved by introducing or improving the efficacy of the forced ventilation or by modifying the barn structure. Monitoring dairy barns with sensors and Precision Livestock Farming techniques can be helpful for future livestock farming to alert farmers on the need for their interventions to respond immediately to unwanted barn living conditions.

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“…Borchers et al [147] explored RF, linear discriminant, and NN for calving prediction in dairy cattle by examining their behaviour which includes number the of steps, lying time, standing time, transition from one state to other and total motion 14 days before the predicted calving date. Although, the innovative algorithms play a crucial role in livestock management but combining livestock data with public data will improve precision livestock farming standards [148]. Table 8 presents a comparative study of different ML algorithms for livestock production and management.…”

Section: E Livestock Production and Managementmentioning

confidence: 99%

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

et al. 2021

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Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

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The role of interoperable data standards in precision livestock farming in extensive livestock systems: A review

Cited by 96 publications

References 81 publications

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

A Survey of Dairy Cattle Behavior in Different Barns in Northern Italy

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

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