Digital Livestock Farming

Neethirajan, Suresh; Kemp, B.

doi:10.1016/j.sbsr.2021.100408

Cited by 163 publications

(117 citation statements)

References 61 publications

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“…Biometric sensors and artificial intelligence (AI) techniques in digital livestock farming are emerging tools in modern agriculture (Neethirajan & Kemp, 2021). The applications of these tools not only facilitate administering the health of livestock but can also allow for precision monitoring of the intensity of grazing in rangelands.…”

Section: Regenerative Agriculture: the Sustainable Transition Of Agricultural Practicesmentioning

confidence: 99%

Regenerative agriculture and integrative permaculture for sustainable and technology driven global food production and security

et al. 2021

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Intensive agriculture can cause a loss in biodiversity and concerns for global food security. Regenerative agriculture and integrative permaculture are keys to address food security. Digital agricultural tools can aid in attaining agricultural and environmental sustainability. Precision agriculture should be implemented as an agricultural decision support system. Artificial intelligence and machine learning can guide integrated agricultural input management.

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Section: Regenerative Agriculture: the Sustainable Transition Of Agricultural Practicesmentioning

confidence: 99%

Regenerative agriculture and integrative permaculture for sustainable and technology driven global food production and security

et al. 2021

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“…Technological approaches such as PLF can provide solutions to reduce air, soil and water pollutants including ammonia, nitrous oxide, carbon dioxide and methane (Bell et al, 2011;Hou, Velthof and Oenema, 2015;Dominiak and Kristensen, 2017;Todde et al, 2017;Balaine et al, 2020), as well as to reduce energy consumption (Todde et al, 2017) and enhance economic sustainability (Balaine et al, 2020). Furthermore, the constant monitoring of different aspects also allows traceability of the products, providing a more transparent method to the consumer, too (Neethirajan and Kemp, 2021). PLF offers a promising connection between different economic, environmental and societal sustainability aspects, including health conditions, welfare, production and sustainability (Lovarelli, Bacenetti and Guarino, 2020).…”

Section: Impact Of Precision Livestock Farming On Society and The Planetmentioning

confidence: 99%

“…Types of data that can be collected are animal growth, productivity (such as milk, meat or egg production), diseases, animal behavior, fertility, and physical / environmental parameters, such as the temperature, humidity and lighting (Halachmi and Guarino, 2016). PLF technologies interlink such processes to form a complex network through artificial intelligence, and based on the collected data, management decisions could be taken to prevent injury and disease, improve overall welfare and enhance production efficiency (Neethirajan and Kemp, 2021). Warnings, notifications and alarms could be triggered through continuous data collection based on multiple sensors.…”

Section: Introductionmentioning

confidence: 99%

Ethics of Digital Animal Farming

Neethirajan¹

2021

Preprint

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The demand for animal products is expected to continue to rise, which requires the development of efficient livestock farming systems. Environmental, societal and economic concerns regarding this industry are however accumulating, addressing the large resource demand, pollutants and greenhouse gas emissions and health concerns that the livestock industry is responsible for. Precision livestock farming systems allow the continuous automatic monitoring of various physiological, behavioural and phenotypic parameters of animals in order to increase productivity and animal welfare while controlling and minimizing the environmental impact. There is a high potential for digital farming to be the solution for responsibly and ethically feeding the growing and urbanizing population. However, many problems and concerns are still present in this developing industry and remain relatively unaddressed, starting with the ethical aspects in regard to the animal, including its objectification, human-animal relationships and welfare and ending with the societal implications of this digitalization. Concrete frameworks, inter-disciplinary studies and global legislation need to be put in place in order to ensure the safety and protection of the animals, farmer and society. Here, implications of digital farming for the animals, farmers, society and the planet are critically reviewed with the future outlook of digital farms.

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“…Digital technologies, in particular, precision livestock farming, and artificial intelligence have the potential to shape transformation in animal welfare [1]. To ensure access to sustainable and high-quality health attention and welfare in animal husbandry management, innovative tools are needed.…”

Section: Introductionmentioning

confidence: 99%

Happy Cow or Thinking Pig? WUR Wolf—Facial Coding Platform for Measuring Emotions in Farm Animals

Neethirajan

2021

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Emotions play an indicative and informative role in the investigation of farm animal behaviors. Systems that respond and can measure emotions provide a natural user interface in enabling the digitalization of animal welfare platforms. The faces of farm animals can be one of the richest channels for expressing emotions. WUR Wolf (Wageningen University & Research: Wolf Mascot), a real-time facial recognition platform that can automatically code the emotions of farm animals, is presented in this study. The developed Python-based algorithms detect and track the facial features of cows and pigs, analyze the appearance, ear postures, and eye white regions, and correlate these with the mental/emotional states of the farm animals. The system is trained on a dataset of facial features of images of farm animals collected in over six farms and has been optimized to operate with an average accuracy of 85%. From these, the emotional states of animals in real time are determined. The software detects 13 facial actions and an inferred nine emotional states, including whether the animal is aggressive, calm, or neutral. A real-time emotion recognition system based on YoloV3, a Faster YoloV4-based facial detection platform and an ensemble Convolutional Neural Networks (RCNN) is presented. Detecting facial features of farm animals simultaneously in real time enables many new interfaces for automated decision-making tools for livestock farmers. Emotion sensing offers a vast potential for improving animal welfare and animal–human interactions.

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Digital Livestock Farming

Cited by 163 publications

References 61 publications

Regenerative agriculture and integrative permaculture for sustainable and technology driven global food production and security

Regenerative agriculture and integrative permaculture for sustainable and technology driven global food production and security

Ethics of Digital Animal Farming

Happy Cow or Thinking Pig? WUR Wolf—Facial Coding Platform for Measuring Emotions in Farm Animals

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