Title:Global Food Production and Distribution Analysis using Data Mining and
Unsupervised Learning
Volume: 14
Issue: 1
Author(s): Himanshu Shekhar and Abhilasha Sharma*
Affiliation:
- Department of Software Engineering, Delhi Technological University, New Delhi, 110042, India
Keywords:
Food industry, data mining, clustering, food distribution, agricultural credit, economic foundations.
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.
