Invited review: Sensor technologies for real-time monitoring of the rumen environment

Han, Chan Su; Kaur, Upinder; Bai, Huiwen; Reis, Barbara Roqueto dos; White, Robin R.; Nawrocki, Robert A.; Voyles, Richard M.; Kang, Min Gyu; Priya, Shashank

doi:10.3168/jds.2021-20576

Cited by 10 publications

(7 citation statements)

References 170 publications

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“…Several non-invasive methods are available to measure methane production in animals. These include microbial biomarkers in the rumen that, if heritable, could be used for targeting purposes, GHG emission monitoring systems using hand-held methane sensors, and ingestible methane detection capsules and other sensors that allow continuous monitoring with Wi-Fi [133][134][135] . However, all these methods currently have some disadvantages such as cost, reliability and/or reproducibility, so wider uptake of these innovations tends to depend to some extent on the development of appropriate validation, calibration and standardization protocols.…”

Section: Review Articlementioning

confidence: 99%

Assessing and addressing the global state of food production data scarcity

Kebede,

Abou Ali,

Clavelle

et al. 2024

Nat Rev Earth Environ

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Food production data -such as crop, livestock, aquaculture and fisheries statistics -are critical to achieving multiple sustainable development goals. However, the lack of reliable, regularly collected, accessible, usable and spatially disaggregated statistics limits an accurate picture of the state of food production in many countries and prevents the implementation of effective food system interventions. In this Review, we take stock of national and international food production data to understand its availability and limitations. Across databases, there is substantial global variation in data timeliness, granularity (both spatially and by food category) and transparency. Data scarcity challenges are most pronounced for livestock and aquatic food production. These challenges are largely concentrated in Central America, the Middle East and Africa owing to a combination of inconsistent census implementation and a global reliance on self-reporting. Because data scarcity is the result of technical, institutional and political obstacles, solutions must include technological and policy innovations. Fusing traditional and emerging data-gathering techniques with coordinated governance and dedicated long-term financing will be key to overcoming current obstacles to sustained, up-to-date and accurate food production data collection, foundational in promoting and monitoring progress towards healthier and more sustainable food systems worldwide.Sections Agricultural Monitoring (GEOGLAM) Crop Monitor, the US Department of Agriculture -Foreign Agricultural Service (USDA-FAS), the FAO, the European Commission Joint Research Center's Anomaly hot Spots of Agricultural Production (ASAP), the MIRCA2000 dataset 18 and the dataset published by Sacks et al. 28 . These data are typically provided at the national or subnational level (administrative levels 0 and 1); Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Section: Review Articlementioning

confidence: 99%

Assessing and addressing the global state of food production data scarcity

Kebede,

Abou Ali,

Clavelle

et al. 2024

Nat Rev Earth Environ

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“…Five primary research perspectives drive existing PLF research: animal science, veterinary medicine, computer science, agricultural engineering, and environmental science. The animal science perspective aims to optimize animal feeding and management by leveraging sensors and intelligent monitoring systems to real-time monitor and analyze animal behavior, physiological states, and environmental factors [ 11 , 12 ]. The veterinary medicine perspective focuses on intelligent prevention and diagnosis of animal diseases [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Precision Livestock Farming Research: A Global Scientometric Review

Jiang

Tang

Cui

et al. 2023

Animals

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Precision livestock farming (PLF) utilises information technology to continuously monitor and manage livestock in real-time, which can improve individual animal health, welfare, productivity and the environmental impact of animal husbandry, contributing to the economic, social and environmental sustainability of livestock farming. PLF has emerged as a pivotal area of multidisciplinary interest. In order to clarify the knowledge evolution and hotspot replacement of PLF research, based on the relevant data from the Web of Science database from 1973 to 2023, this study analyzed the main characteristics, research cores and hot topics of PLF research via CiteSpace. The results point to a significant increase in studies on PLF, with countries having advanced livestock farming systems in Europe and America publishing frequently and collaborating closely across borders. Universities in various countries have been leading the research, with Daniel Berckmans serving as the academic leader. Research primarily focuses on animal science, veterinary science, computer science, agricultural engineering, and environmental science. Current research hotspots center around precision dairy and cattle technology, intelligent systems, and animal behavior, with deep learning, accelerometer, automatic milking systems, lameness, estrus detection, and electronic identification being the main research directions, and deep learning and machine learning represent the forefront of current research. Research hot topics mainly include social science in PLF, the environmental impact of PLF, information technology in PLF, and animal welfare in PLF. Future research in PLF should prioritize inter-institutional and inter-scholar communication and cooperation, integration of multidisciplinary and multimethod research approaches, and utilization of deep learning and machine learning. Furthermore, social science issues should be given due attention in PLF, and the integration of intelligent technologies in animal management should be strengthened, with a focus on animal welfare and the environmental impact of animal husbandry, to promote its sustainable development.

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“…Researchers have embraced advanced machine learning techniques to develop cutting-edge methods for acidosis detection in bovine. Continuous rumen pH monitoring using wireless pH sensors has been explored to provide real-time insights into rumen health and acidosis risk (Han et al, 2022). Analyzing changes in rumination behavior allows for early identification of acidotic conditions.…”

mentioning

confidence: 99%

Investigation of Bovine Disease and Events through Machine Learning Models

Nadeem,

Anis

2024

PJAR

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T he dairy industry plays a pivotal role in global food security by providing essential nutrition through milk production, relies heavily on the health and productivity of dairy cows. However, ensuring the health and well-being of dairy cows is of paramount importance for sustaining a thriving dairy industry, making bovine diseases a top concern for farmers worldwide. Bovine diseases, such as calving complications, mastitis, estrus, acidosis, and lameness, pose significant challenges. Detecting and diagnosing Abstract | Bovine disease identification utilizing multiple information sources and methods is widely applicate in the field of bovine disease prevention and health monitoring. Bovine disease detection is an emerging subject today to accomplish the farms demands of individuals across the globe. This research delves into the realm of bovine disease and event detection using advanced Machine Learning (ML) techniques. Focusing on the critical events of estrus, acidosis, mastitis, lameness, and calving, our study aims to revolutionize disease identification and timely intervention within the dairy industry. By leveraging four distinct ML models-Random Forest, XGBoost, Logistic Regression, and Single Perceptron we meticulously analyze four diverse data-sets to uncover intricate patterns and unveil hidden insights. The efficiency of random sampling in resolving the class imbalance issue is tested along with the validity and adaptability of these models utilizing GridSearchCV optimum parameter modification. The performance evaluation is based on accuracy, precision, recall, F1 score, and Area Under the Curve (AUC) metrics. With a resounding highest accuracy metric, the Random Forest model achieves a notable accuracy of up to 98.25%, while the recall score of 100, and Precision up to 97% affirming its supremacy in classifying bovine events. This achievement underscores the efficacy of employing ML algorithms for accurate and timely disease identification. This ground-breaking fusion of ML techniques with bovine disease detection holds trans-formative potential, promising to elevate animal welfare standards, optimize dairy productivity, and usher in a new era of datadriven dairy management.

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Invited review: Sensor technologies for real-time monitoring of the rumen environment

Cited by 10 publications

References 170 publications

Assessing and addressing the global state of food production data scarcity

Assessing and addressing the global state of food production data scarcity

Precision Livestock Farming Research: A Global Scientometric Review

Investigation of Bovine Disease and Events through Machine Learning Models

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