Assessment of knowledge, attitude, and practice on livestock traceability among cattle farmers and cattle traders in peninsular Malaysia and its impact on disease control

Salina, A.; Hassan, Latiffah; Saharee, Abdul Aziz; Jajere, Saleh Mohammed; Stevenson, Mark; Ghazali, Khadizah

doi:10.1007/s11250-020-02458-5

Cited by 9 publications

(3 citation statements)

References 10 publications

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“…The role of precision livestock farming is getting more and more significant by supporting the decision-making processes of livestock owners and changing their role. It can also facilitate the products’ traceability, in addition to monitoring their quality and the living conditions of animals, as required by policy-makers [ 71 ]. Precision livestock farming relies on non-invasive sensors, such as cameras, accelerometers, gyroscopes, radio-frequency identification systems, pedometers, and optical and temperature sensors [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Agriculture: A Comprehensive Updated Review

Benos

Tagarakis

Dolias

et al. 2021

Sensors

428

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

428

146

View full text Add to dashboard Cite

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“…By using such a decision support system and real time monitoring of the livestock the quality policies can be implemented regarding living condition, food plan, vaccination etc. [36].…”

Section: Livestock Managementmentioning

confidence: 99%

Review of Machine Learning Model Applications in Precision Agriculture

Baburao¹,

Kulkarni²,

Kharade³

et al. 2023

Advances in Computer Science Research

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Over the past two decades, modern agriculture has made significant advancements. The methods used in farming have changed from conventional ways to digital technologies as a result of significant technology improvement. Advances in machine learning and artificial intelligence are being applied in this discipline to reevaluate farming practices in order to meet the demands of an expanding population. Throughout the entire cycle of planting, growing, and harvesting, machine learning is prevalent. It starts with the planting of a seed in the ground, goes through soil preparation, seed breeding, crop health monitoring, measuring water feed and concludes with the harvest being picked up by robots by using computer vision techniques.For crop selection, yield prediction, soil classification, weather forecasting, irrigation system, fertilizer prescription, disease prediction, and determining the minimal support price, machine learning models are developed in the field of precision agriculture. In this article we will cover the different categories of precision agriculture applications and use of machine learning models in those different categories. Various models in precision agriculture include Artificial Neural Networks, Support Vector Machines (SVMs), Convolution Neural Networks (CNN), Random Forest (RF), K-Nearest Neighbor (KNN), K-Means Clustering. The ultimate solution to issues in agriculture rests in the efficient application of Machine Learning (ML). ML can bring about a paradigm change in nations like India where agriculture is the main source of employment. Since most Indian rural areas have adopted digitalization, ML and AI-related applications are gradually emerging in this sector.

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“…Precision livestock farming is becoming increasingly important in supporting livestock owners' decision-making processes and changing their roles. It can also help with product traceability and monitoring product quality and animal living conditions, as required by policymakers (Salina et al, 2020). Cameras, accelerometers, gyroscopes, radiofrequency identification systems, pedometers, and optical and temperature sensors are used in precision livestock farming (Li et al, 2020).…”

Section: Water Managementmentioning

confidence: 99%

Possibility of Using Machine Learning Algorithms to Modernize Agricultural Research in Indonesia

Muditomo,

Syamsari

2023

J. Ag. Rural Econ.

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Even though many researchers and campuses have agricultural technology study programs, various technological and agricultural research in Indonesia still needs to be fully integrated. The author believes that technological advancements, particularly in Artificial Intelligence and Machine Learning, provide substantial advantages in various industries, including agriculture. This paper will examine the opportunities for using machine learning algorithms in precision agricultural research to inspire Indonesian agricultural researchers.

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Assessment of knowledge, attitude, and practice on livestock traceability among cattle farmers and cattle traders in peninsular Malaysia and its impact on disease control

Cited by 9 publications

References 10 publications

Machine Learning in Agriculture: A Comprehensive Updated Review

Machine Learning in Agriculture: A Comprehensive Updated Review

Review of Machine Learning Model Applications in Precision Agriculture

Possibility of Using Machine Learning Algorithms to Modernize Agricultural Research in Indonesia

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