减少哥伦比亚可可小农户信息不对称的人工智能解决方案

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY Information Processing in Agriculture Pub Date : 2024-09-01 DOI:10.1016/j.inpa.2023.03.001
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引用次数: 0

摘要

信息的缺乏给哥伦比亚的小规模农户带来了问题,因为这阻碍了他们以公平的价格销售和了解高效的生产技术。在世界各地,许多技术干预措施已被证明有助于减少信息不对称。因此,我们提出了一种基于遗传算法和自然语言处理器(NLP)的技术方案,使生产者能够通过信息处理获得知识。此外,我们还在哥伦比亚不同地区的 20 个城市进行了实地考察,并对 500 名哥伦比亚可可小农进行了调查。这项实地调查有助于我们确定小规模农户的需求、市场条件以及人工智能(AI)工具的相关性。结果表明,人工智能方法可以通过提供有关价格、天气和生产技术的信息,改善小农户的经济状况。实地工作证明,只有在动态贸易周期的情况下,技术工具才是一个好的选择。人工智能工具可以传输和处理信息,使之成为生产者的知识,帮助他们发展成集体战略。该方法结合了遗传算法、NLP 和可可种植的实地调查,是一种有助于减少信息不对称的新方法。我们为有关采用人工智能工具更好地发展可可小规模种植业的文献做出了贡献。
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Artificial intelligence solutions to reduce information asymmetry for Colombian cocoa small-scale farmers

The lack of information creates problems for Colombian small-scale farmers, as it impedes them from selling at fair prices and knowing efficient production techniques. Around the world, many technological interventions have proven helpful in reducing information asymmetries. Therefore, we proposed a technological scheme based on a genetic algorithm and a natural language processor (NLP) that enables producers to obtain knowledge through information processing. Also, we ran fieldwork in twenty municipalities and a survey among 500 Colombian cocoa small-scale farmers in different regions in Colombia. This fieldwork helps us determine small-scale farmers' necessities, market conditions, and the relevance of an Artificial Intelligence (AI) tool. The results have shown that AI methodologies could improve the economic conditions of small farmers by providing access to information on prices, weather, and production techniques. The fieldwork evidence that a technological tool is a good option only if there are dynamic trade cycles. AI tools could transmit and process information to become producers' knowledge and help them evolve into collective strategies. The methodology, which combines genetic algorithms, NLP, and fieldwork for cocoa farming, is a novelty that contributes to information asymmetry reduction. We contributed to the literature about adopting AI tools to develop cocoa small-scale farming better.

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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
期刊最新文献
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