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Recent Advances in Food, Nutrition & Agriculture

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ISSN (Print): 2772-574X
ISSN (Online): 2772-5758

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Research Article

Global Food Production and Distribution Analysis using Data Mining and Unsupervised Learning

Author(s): Himanshu Shekhar and Abhilasha Sharma*

Volume 14, Issue 1, 2023

Published on: 04 April, 2023

Page: [57 - 70] Pages: 14

DOI: 10.2174/2772574X14666230126095121

Price: $65

Abstract

Background: Today’s food industry is extensive and complicated, encompassing anything from subsistence agriculture to multinational food corporations. The mobility of food and food elements in food systems has a major impact on biodiversity preservation and the overall sustainability of our fragile global ecosystem. Identifying the human and livestock consumption patterns across regions and territories will optimize the dietary standards of the habitually undernourished and the expanding population without substantially increasing the amount of land under cultivation. Food preservation is the basis for economic advancement and social sustainability, so the food industry, both local and global, is fundamental to everyone. As a primary mechanism for ensuring global food preservation, there is currently a strong emphasis on accelerating food supply and decreasing waste. Thus, analyzing the production and distribution of food supply will boost economic sustainability.

Methods: In this paper, we present a quantitative analysis of global and regional food supply to reveal the flow of food and feed products in various parts of the world. Using data mining and machine learning-based approaches, we seek to quantify the production and distribution of food elements. The study aims to employ artificial intelligence-based methods to comprehend the shift and change in supply and consumption patterns with timely distribution to meet the global food instability. The method involves using statistical-based approaches to identify the hidden factors and variables. Feature engineering is used to uncover the interesting features in the dataset, and various clustering-based algorithms, like K-Means, have been utilized to group and identify the similar and most notable features.

Results: The concept of data mining and machine learning-based algorithms has helped us in identifying the global food production and distribution subsystem. The identified elements and their relationship can help stakeholders in regulating various external and internal factors, including urbanization, urban food needs, the economic, political and social framework, food demand, and supply flows. The exploratory analysis helps in establishing the efficiency and dynamism of food supply and distribution systems.

Conclusion: The outcome demonstrates a pattern indicating the flow of currently grown crops into various endpoints. Few countries with massive populations have shown tremendous growth in their production capacity. Despite the fact that only a few countries produce a large portion of food and feed crops, still it is insufficient to feed the estimated global population. Significant changes in many people's socioeconomic conditions, as well as radical dietary changes, will also be required to boost agricultural credit and economic foundations.

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