A systematic literature review on the use of machine learning in precision livestock farming

García, Ricardo Hernández; Aguilar, José; Toro, Mauricio; Pinto, Angel; Rodríguez, Paul

doi:10.1016/j.compag.2020.105826

Cited by 171 publications

(79 citation statements)

References 54 publications

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“…Due to the broad range of applications of ML in agriculture, several reviews have been published in this research field. The majority of these review studies have been dedicated to crop disease detection [ 13 , 14 , 15 , 16 ], weed detection [ 17 , 18 ], yield prediction [ 19 , 20 ], crop recognition [ 21 , 22 ], water management [ 23 , 24 ], animal welfare [ 25 , 26 ], and livestock production [ 27 , 28 ]. Furthermore, other studies were concerned with the implementation of ML methods regarding the main grain crops by investigating different aspects including quality and disease detection [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

424

146

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The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

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Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

424

146

View full text Add to dashboard Cite

show abstract

“…The change in activity prior to diagnosis was different from healthy "control" heifers and different from the ill heifer's previous activity. More research is needed to determine robust algorithms for detecting disease, perhaps specific diseases (García et al, 2020). Also, GPS tracking and other sensors may be useful for detecting disease.…”

Section: Remotely Monitoring For Livestock Diseasementioning

confidence: 99%

“…Cheng et al (2014) used a locally sensitive Bloom filter to reduce the size of sensor data. Edge computing can use historic data and machine learning processes (García et al, 2020) such as random forests and signal vector machine to detect important states or events from data streams obtained from sensors (Park et al, 2018). Hu et al (2016) demonstrated that edge computing reduced response time and energy use of mobile devices.…”

Section: Data Processing and Transfermentioning

confidence: 99%

Opportunities to Apply Precision Livestock Management on Rangelands

Bailey

Trotter

Tobin

et al. 2021

Front. Sustain. Food Syst.

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Precision livestock management has become a new field of study as the result of recent advancements in real-time global positioning system (GPS) tracking, accelerometer and other sensor technologies. Real-time tracking and accelerometer monitoring has the potential to remotely detect livestock disease, animal well-being and grazing distribution issues and notify ranchers and graziers so that they can respond as soon as possible. On-going research has shown that accelerometers can remotely monitor livestock behavior and detect activity changes that are associated with disease and parturition. GPS tracking can also detect parturition by monitoring the distance between a ewe and the remainder of the flock. Tracking also has the potential to detect water system failures. Combinations of GPS tracking and accelerometer monitoring may be more accurate than either device used by itself. Real-time GPS tracking can identify when livestock congregate in environmental sensitive areas which may allow managers the chance to respond before resource degradation occurs. Identification of genetic markers associated with terrain use, decreased cost of GPS tracking and novel tracking data processing should facilitate development of tools needed for genetic selection for cattle grazing distribution. Precision livestock management has potential to improve welfare of livestock grazing rangelands and forested lands, reduce labor costs and improve ranch profitability and improve the condition and sustainability of riparian areas and other environmental sensitive areas on grazing lands around the world.

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“…The development of artificial intelligence (AI), especially machine learning, makes everything easier to apply. In accordance with its advantages, one part of ML is a supervised learning algorithm, which can be used not only for control and monitoring systems [2], but also in data science where it can be implemented in a data classification that applies in various fields, such as health [3], livestock and agriculture [4], [5], economy and industry [6], transportation [7], education, health [8] and others [9].…”

Section: Introductionmentioning

confidence: 99%

SentiHotel: a sentiment analysis application of hotel services using an optimized neural network

et al. 2021

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An assessed hotel service is necessary for tourists and everyone who is traveling, however currently it is still difficult to find recommended hotel information. The solution provided in this research is to propose a smart application that has been developed by implementing machine learning in it. The purpose is to build a sentiment review smart application by applying the sentiment analysis hybrid model of the best neural network (NN) algorithm model that has been optimized using genetic algorithms. To get the right model, the research method was carried out with experiments starting from the initial stages of conducting data preprocessing, tokenization, weighting, modeling experiments, and conducting the system evaluation stage to determine the success of the proposed model. The progress of the application development system is by using the prototyping model. SentiHotel is a sentiment application that was successfully built to provide a solution for tourists in assessing a hotel service. The software validation test is carried out using the blackbox method and the results show that the SentHotel application is in accordance with the expected result; all system functions can run properly.

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A systematic literature review on the use of machine learning in precision livestock farming

Cited by 171 publications

References 54 publications

Machine Learning in Agriculture: A Comprehensive Updated Review

Machine Learning in Agriculture: A Comprehensive Updated Review

Opportunities to Apply Precision Livestock Management on Rangelands

SentiHotel: a sentiment analysis application of hotel services using an optimized neural network

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