Crop Recommendation Systems Based on Soil and Environmental Factors Using Graph Convolution Neural Network: A Systematic Literature Review

Ayesha Barvin, P.; Sampradeepraj, T.

doi:10.3390/ecsa-10-16010

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2024

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Cited by 3 publications

References 26 publications

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A Systematic Review of the Impact of Auxiliary Information on Recommender Systems

Ayemowa,

Ibrahim,

Adamu Bena

2024

IEEE Access

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A Systematic Review of the Impact of Auxiliary Information on Recommender Systems

Ayemowa,

Ibrahim,

Adamu Bena

2024

IEEE Access

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Machine Learning Based Crop Recommendation System

Deo,

Bandhu,

Litoriya

et al. 2024

2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET)

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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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Crop Recommendation Systems Based on Soil and Environmental Factors Using Graph Convolution Neural Network: A Systematic Literature Review

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cited by 3 publications

References 26 publications

A Systematic Review of the Impact of Auxiliary Information on Recommender Systems

A Systematic Review of the Impact of Auxiliary Information on Recommender Systems

Machine Learning Based Crop Recommendation System

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

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