Machine Learning in Agriculture: A Land Data Approach to Optimize Crop Choice with the LAGNet Model

Kamatchi, C. Bala; Muthukumaravel, A.

doi:10.1109/esci59607.2024.10497261

2024 International Conference on Emerging Smart Computing and Informatics (ESCI) 2024

DOI: 10.1109/esci59607.2024.10497261

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Machine Learning in Agriculture: A Land Data Approach to Optimize Crop Choice with the LAGNet Model

C. Bala Kamatchi,

A. Muthukumaravel

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2024

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Harnessing the power of machine learning for crop improvement and sustainable production

Khatibi,

Ali

2024

Front. Plant Sci.

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Crop improvement and production domains encounter large amounts of expanding data with multi-layer complexity that forces researchers to use machine-learning approaches to establish predictive and informative models to understand the sophisticated mechanisms underlying these processes. All machine-learning approaches aim to fit models to target data; nevertheless, it should be noted that a wide range of specialized methods might initially appear confusing. The principal objective of this study is to offer researchers an explicit introduction to some of the essential machine-learning approaches and their applications, comprising the most modern and utilized methods that have gained widespread adoption in crop improvement or similar domains. This article explicitly explains how different machine-learning methods could be applied for given agricultural data, highlights newly emerging techniques for machine-learning users, and lays out technical strategies for agri/crop research practitioners and researchers.

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Harnessing the power of machine learning for crop improvement and sustainable production

Khatibi,

Ali

2024

Front. Plant Sci.

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show abstract

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Machine Learning in Agriculture: A Land Data Approach to Optimize Crop Choice with the LAGNet Model

Cited by 1 publication

References 17 publications

Harnessing the power of machine learning for crop improvement and sustainable production

Harnessing the power of machine learning for crop improvement and sustainable production

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