利用数据挖掘和无监督学习进行全球食品生产和分销分析。

Himanshu Shekhar, Abhilasha Sharma
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引用次数: 0

摘要

背景:当今的食品工业广泛而复杂,从自给农业到跨国食品公司,无所不包。粮食和粮食要素在粮食系统中的流动性对保护生物多样性和我们脆弱的全球生态系统的整体可持续性有着重大影响。确定跨地区和领土的人类和牲畜消费模式,可以在不大幅增加耕地面积的情况下,优化习惯性营养不良者和不断扩大的人口的饮食标准。粮食保障是经济进步和社会可持续发展的基础,因此,粮食产业,无论是地方性的还是全球性的,对每个人都至关重要。作为确保全球粮食保鲜的主要机制,目前非常强调加快粮食供应和减少浪费。因此,对粮食供应的生产和分配进行分析将促进经济的可持续发展:在本文中,我们对全球和地区粮食供应进行了定量分析,以揭示世界各地粮食和饲料产品的流向。利用数据挖掘和基于机器学习的方法,我们试图量化粮食要素的生产和分配。这项研究旨在采用基于人工智能的方法来理解供应和消费模式的转变和变化,并及时进行分配,以应对全球粮食不稳定的问题。该方法包括使用基于统计的方法来识别隐藏的因素和变量。特征工程用于发现数据集中的有趣特征,各种基于聚类的算法(如 K-Means 算法)用于分组和识别相似和最显著的特征:数据挖掘的概念和基于机器学习的算法帮助我们确定了全球粮食生产和分配子系统。确定的要素及其关系有助于利益相关者调节各种外部和内部因素,包括城市化、城市粮食需求、经济、政治和社会框架、粮食需求和供应流。探索性分析有助于确定粮食供应和分配系统的效率和活力:分析结果显示了目前种植的农作物流向不同终端的模式。人口众多的少数几个国家的生产能力出现了巨大增长。尽管只有少数几个国家生产了大部分粮食和饲料作物,但仍不足以养活估计的全球人口。许多人的社会经济条件需要发生重大变化,饮食习惯也需要彻底改变,以促进农业信贷和经济基础的发展。
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Global Food Production and Distribution Analysis using Data Mining and Unsupervised Learning.

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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