CYBELE – Fostering precision agriculture &amp; livestock farming through secure access to large-scale HPC enabled virtual industrial experimentation environments fostering scalable big data analytics

Perakis, Κonstantinos; Lampathaki, Fenareti; Nikas, Konstantinos; Georgiou, Yiannis; Marko, Oskar; Maselyne, Jarissa

doi:10.1016/j.comnet.2019.107035

Cited by 46 publications

(31 citation statements)

References 7 publications

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“…Another challenge concerns data storage capacity and strong computational power. However, there are already efforts to design a set of industrial, large-scale high-performance computing solutions to support the processing of very large PLF data sets from different users ( 94 ).…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review on Commercially Available and Validated Sensor Technologies for Welfare Assessment of Dairy Cattle

et al. 2021

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In order to base welfare assessment of dairy cattle on real-time measurement, integration of valid and reliable precision livestock farming (PLF) technologies is needed. The aim of this study was to provide a systematic overview of externally validated and commercially available PLF technologies, which could be used for sensor-based welfare assessment in dairy cattle. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic literature review was conducted to identify externally validated sensor technologies. Out of 1,111 publications initially extracted from databases, only 42 studies describing 30 tools (including prototypes) met requirements for external validation. Moreover, through market search, 129 different retailed technologies with application for animal-based welfare assessment were identified. In total, only 18 currently retailed sensors have been externally validated (14%). The highest validation rate was found for systems based on accelerometers (30% of tools available on the market have validation records), while the lower rates were obtained for cameras (10%), load cells (8%), miscellaneous milk sensors (8%), and boluses (7%). Validated traits concerned animal activity, feeding and drinking behavior, physical condition, and health of animals. The majority of tools were validated on adult cows. Non-active behavior (lying and standing) and rumination were the most often validated for the high performance. Regarding active behavior (e.g., walking), lower performance of tools was reported. Also, tools used for physical condition (e.g., body condition scoring) and health evaluation (e.g., mastitis detection) were classified in lower performance group. The precision and accuracy of feeding and drinking assessment varied depending on measured trait and used sensor. Regarding relevance for animal-based welfare assessment, several validated technologies had application for good health (e.g., milk quality sensors) and good feeding (e.g., load cells, accelerometers). Accelerometers-based systems have also practical relevance to assess good housing. However, currently available PLF technologies have low potential to assess appropriate behavior of dairy cows. To increase actors' trust toward the PLF technology and prompt sensor-based welfare assessment, validation studies, especially in commercial herds, are needed. Future research should concentrate on developing and validating PLF technologies dedicated to the assessment of appropriate behavior and tools dedicated to monitoring the health and welfare in calves and heifers.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review on Commercially Available and Validated Sensor Technologies for Welfare Assessment of Dairy Cattle

et al. 2021

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“…Furthermore, at production stage articles [29] identifies changes due to temperature and relative humidity in flavor, texture, shelf life, nutritional attributes, aroma, among others, once the product harvest occurs for different fresh fruits and vegetables. Same as [30] suggests permanent monitoring of crops to minimize risks to face dangerous levels of temperature and humidity.…”

Section: Influence Of Variables At Cultivation Production and Post-pr...mentioning

confidence: 99%

An Adaptive Sampling Period Approach for Management of IoT Energy Consumption: Case Study Approach

Rodriguez-Pabon

Riva

Zerbini

et al. 2022

Sensors

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The Internet of Things (IoT) opens opportunities to monitor, optimize, and automate processes into the Agricultural Value Chains (AVC). However, challenges remain in terms of energy consumption. In this paper, we assessed the impact of environmental variables in AVC based on the most influential variables. We developed an adaptive sampling period method to save IoT device energy and to maintain the ideal sensing quality based on these variables, particularly for temperature and humidity monitoring. The evaluation on real scenarios (Coffee Crop) shows that the suggested adaptive algorithm can reduce the current consumption up to 11% compared with a traditional fixed-rate approach, while preserving the accuracy of the data.

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“…The ability to monitor animals continuously in real-time throughout their lives and to control their environments means that both productivity and welfare can potentially be improved through early detection of health problems (Wathes et al, 2008;Banhazi et al, 2012;Berckmans, 2017;Veissier et al, 2019), leading to targeted (and therefore reduced) use of medication, lower mortality and improved health. These outcomes in turn have other social benefits such as less waste, greater efficiency and lower environmental impact (Clark and Tilman, 2017;Perakis et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Does Smart Farming Improve or Damage Animal Welfare? Technology and What Animals Want

Dawkins

2021

Front. Anim. Sci.

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“Smart” or “precision” farming has revolutionized crop agriculture but its application to livestock farming has raised ethical concerns because of its possible adverse effects on animal welfare. With rising public concern for animal welfare across the world, some people see the efficiency gains offered by the new technology as a direct threat to the animals themselves, allowing producers to get “more for less” in the interests of profit. Others see major welfare advantages through life-long health monitoring, delivery of individual care and optimization of environmental conditions. The answer to the question of whether smart farming improves or damages animal welfare is likely to depend on three main factors. Firstly, much will depend on how welfare is defined and the extent to which politicians, scientists, farmers and members of the public can agree on what welfare means and so come to a common view on how to judge how it is impacted by technology. Defining welfare as a combination of good health and what the animals themselves want provides a unifying and animal-centered way forward. It can also be directly adapted for computer recognition of welfare. A second critical factor will be whether high welfare standards are made a priority within smart farming systems. To achieve this, it will be necessary both to develop computer algorithms that can recognize welfare to the satisfaction of both the public and farmers and also to build good welfare into the control and decision-making of smart systems. What will matter most in the end, however, is a third factor, which is whether smart farming can actually deliver its promised improvements in animal welfare when applied in the real world. An ethical evaluation will only be possible when the new technologies are more widely deployed on commercial farms and their full social, environmental, financial and welfare implications become apparent.

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CYBELE – Fostering precision agriculture & livestock farming through secure access to large-scale HPC enabled virtual industrial experimentation environments fostering scalable big data analytics

Cited by 46 publications

References 7 publications

A Systematic Review on Commercially Available and Validated Sensor Technologies for Welfare Assessment of Dairy Cattle

A Systematic Review on Commercially Available and Validated Sensor Technologies for Welfare Assessment of Dairy Cattle

An Adaptive Sampling Period Approach for Management of IoT Energy Consumption: Case Study Approach

Does Smart Farming Improve or Damage Animal Welfare? Technology and What Animals Want

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