Machine Learning and Sensor Roles for Improving Livestock Farming Using Big Data

Mazhar, Sarah; Akila, D.

doi:10.1007/978-981-19-2538-2_17

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Internet of Things (IoT) devices and RFID-based electronic identification [8] are employed intensively in smart farming to collect a huge amount of heterogeneous data used to provide several valuable insights. Data from sensors processed with artificial intelligence (AI) and machine learning (ML) have been used in applications on dairy farms recently [9,10]. For example, K-nearest neighbors (KNN), random forest (RF), and extreme boosting algorithm (XGBoost) were applied to classify unitary behaviors of 10 dairy cows [4].…”

Section: Introductionmentioning

confidence: 99%

Health Status Classification for Cows Using Machine Learning and Data Management on AWS Cloud

Dineva,

Atanasova

2023

Animals

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The health and welfare of livestock are significant for ensuring the sustainability and profitability of the agricultural industry. Addressing efficient ways to monitor and report the health status of individual cows is critical to prevent outbreaks and maintain herd productivity. The purpose of the study is to develop a machine learning (ML) model to classify the health status of milk cows into three categories. In this research, data are collected from existing non-invasive IoT devices and tools in a dairy farm, monitoring the micro- and macroenvironment of the cow in combination with particular information on age, days in milk, lactation, and more. A workflow of various data-processing methods is systematized and presented to create a complete, efficient, and reusable roadmap for data processing, modeling, and real-world integration. Following the proposed workflow, the data were treated, and five different ML algorithms were trained and tested to select the most descriptive one to monitor the health status of individual cows. The highest result for health status assessment is obtained by random forest classifier (RFC) with an accuracy of 0.959, recall of 0.954, and precision of 0.97. To increase the security, speed, and reliability of the work process, a cloud architecture of services is presented to integrate the trained model as an additional functionality in the Amazon Web Services (AWS) environment. The classification results of the ML model are visualized in a newly created interface in the client application.

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