Digitalisation of Agricultural Production for Precision Farming: A Case Study

Author:

Mukhamedova Karina R.,Cherepkova Natalya P.,Korotkov Alexandr V.,Dagasheva Zhanerke B.,Tvaronavičienė Manuela

Abstract

The introduction of a digital platform for practical use at an agro-industrial enterprise is of great practical importance for the development of precision farming. Modern digital information systems are an integral part of precision farming and, in many ways, their foundation. During the work on the Farm Management Information Systems (FMIS) project, software and methodological framework for the use of precision farming techniques and information technologies for managing the process of growing crops in the field was developed. The introduction of a digital platform was carried out as an important experiment. Research methods such as bibliographic analysis and statistical processing were used. This study used modelling and statistical estimation of parameters. The findings were used to estimate the volume of transactions. In addition, during the experiment, communication schemes were worked out. The channel for receiving and transmitting information was tested, along with the channel-forming equipment (routers, switches, gateways) and the basic settings. The study checked the integration of the platform with external systems. A test was carried out for the passage of digital signals to the platform, including various electronic forms and reports. The recommendation for the policy planner is to ensure the required accuracy of the results. The practical value of our findings is that the electronic recording and preservation of the history of fieldwork and crops can help agro-industry workers in preparing special reports on the production cycle.

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

Reference34 articles.

1. Strategic Development Plan of the Republic of Kazakhstan Until 2025. 2022.

2. A survey on deep learning for big data;Zhang;Inf. Fusion,2018

3. Machine learning and big data processing: A technological perspective and review;Bhatnagar;Adv. Int. Sys. Comp.,2018

4. Liakos, K., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. Machine learning in agriculture: A review. Sensors, 2018. 18.

5. Research and application of 3D visualization plug-in integration with arcgis;Li;IFIP Adv. Inf. Commun. Tech.,2019

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3