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• W4385819022 abstract "Food security and sustainability issues have been one of the main agendas of all the countries to support the demand of the ever-growing population in the world. With the outbreak of the COVID-19 pandemic, most countries are now vulnerable to food insecurity and non-sustainability. There are numerous policies which have been proposed by the country leaders, including Malaysia, to improve food security and sustainability. There are five main objectives of the Malaysian government in ensuring secured and sustained food production. This study is motivated toward exploring the potential machine learning techniques that can be leveraged to achieve all five main objectives. A thorough review of the usage of machine learning has been carried out in this study. Six critical factors that contribute to food security and sustainability have been focused on in this study, such as (1) livestock management, (2) irrigation management, (3) crop yield, (4) disease detection, (5) weather prediction, and (6) transportation. With all the potential machine learning techniques identified and reviewed, this study aims to guide Malaysia’s policymakers and researchers on exploring the possibilities of adopting machine learning techniques in achieving a secured and sustained food production for the future." @default.
• W4385819022 created "2023-08-15" @default.
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• W4385819022 date "2023-01-01" @default.
• W4385819022 modified "2023-10-01" @default.
• W4385819022 title "Machine Learning in Food Security and Sustainability" @default.
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• W4385819022 doi "https://doi.org/10.1007/978-3-031-04560-8_67" @default.
• W4385819022 hasPublicationYear "2023" @default.
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