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Big Data Driven Smart Agriculture: Pathway for Sustainable Development

机译:大数据驱动的智慧农业:可持续发展之路

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Increasing agricultural production is top most solution in the face of rapid population growth through digitalization of agriculture by using most developed technology like big data. There is a long debate on the application of big data in agriculture. This study is an attempt to explore the suitability of the big data technologies for increasing production and improving quality in agriculture. The study uses an extensive review of current research works and studies in agriculture for exploring the best and compatible practices which can help farmers at field level for increasing production and improving quality. This study reveals a number of available big data technologies and practices in agriculture for solving the current problems and challenges at field level. A conceptual model is developed for proper implementation of available big data technologies at farmer's field level. The study highlights data generation procedure, availability of technology, availability of hardware, software, data collection techniques, method of analysis and suitability of application of big data technologies for smart agriculture. The article explores that there are still some challenges exists in this field as a new domain in agriculture like privacy of data, data quality, availability, initial investment, infrastructure and related expertise. The study suggests that government initiatives, public-private partnership, openness of data, financial investment and regional basis research work are necessary for implementing the big data technologies in agriculture at large scale.
机译:面对人口的快速增长,通过使用最先进的技术(例如大数据)将农业数字化,提高农业产量是最重要的解决方案。关于大数据在农业中的应用,存在着长期的争论。这项研究旨在探索大数据技术对提高产量和改善农业质量的适用性。该研究对农业方面的当前研究工作和研究进行了广泛的综述,以探索最佳和兼容的实践,这些实践可以帮助实地农民提高产量和提高质量。这项研究揭示了农业中可用的许多大数据技术和实践,可以解决田间水平上的当前问题和挑战。开发了一个概念模型,用于在农民田间一级正确实施可用的大数据技术。该研究重点介绍了数据生成过程,技术可用性,硬件,软件可用性,数据收集技术,分析方法以及大数据技术在智能农业中的适用性。本文探讨了该领域作为农业的一个新领域仍然存在一些挑战,例如数据隐私,数据质量,可用性,初始投资,基础设施和相关专业知识。研究表明,政府计划,公私伙伴关系,数据开放性,金融投资和区域基础研究工作对于大规模实施农业大数据技术是必要的。

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