Open Access

An Intelligent Systems-Based Evaluation Model of Rural Agricultural Development in China Inspired by International Precision Farming Technologies

https://doi.org/10.55640/ijnget-v03i07-01

PDF

Dr. Wei Zhang ¹ , Prof. Liying Chen ¹ ,

⁴ School of Intelligent Engineering, Tsinghua Institute of Advanced Technology, Beijing, China

⁴ Department of Computer Engineering, Shanghai Future Technology University, Shanghai, China

Abstract

The rapid evolution of precision agriculture and intelligent farming systems has significantly transformed global agricultural production paradigms, offering data-driven, efficient, and sustainable solutions to traditional rural development challenges. This study proposes an intelligent systems-based evaluation model for assessing rural agricultural development in China, drawing insights from international precision farming technologies. The research integrates big data analytics, machine learning, IoT-enabled agricultural systems, and policy-driven agricultural modernization frameworks to construct a multi-layered evaluation architecture. Building on prior advancements in smart farming and agricultural digitalization (Alfred, 2021), the study synthesizes global practices and adapts them to the Chinese rural agricultural context, emphasizing productivity, sustainability, and technological adoption. The methodology employs a hybrid analytical framework combining indicator-based evaluation, system dynamics modeling, and intelligent decision-support mechanisms. Findings suggest that precision agriculture technologies significantly enhance resource efficiency, yield optimization, and environmental sustainability, while also revealing gaps in technological accessibility and regional implementation disparities. The study contributes to the theoretical advancement of intelligent agricultural evaluation systems and provides actionable insights for policymakers and agricultural planners aiming to modernize rural development systems in China.

Keywords

Precision agriculture, intelligent systems, rural development, China agriculture

References

R. Alfred, "Towards paddy rice smart farming: a review on big data, machine learning, and rice production tasks," IEEE Access, vol. 9, pp. 50358-50380, 2021.

R. D. Andrimont, "Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union," Scientific data, vol. 7, no. 1, p. 352, 2020.

A. Balafoutis, "Precision agriculture technologies positively contributing to GHG emissions mitigation, farm productivity and economics," Sustainability, vol. 9, no. 8, p. 1339, 2017.

K. Coble, "Advancing US agricultural competitiveness with big data and agricultural economic market information, analysis, and research," 2016.

farming: Agriculture's shift from a labor intensive to technology native industry," Internet of Things, vol. 9, p. 100142, 2020.

H. Friedmann, "Changes in the international division of labor: agri-food complexes and export agriculture," in Towards a new political economy of agriculture: Routledge, 2021, pp. 65-93.

A. Gandomi and M. Haider, "Beyond the hype: Big data concepts, methods, and analytics," International journal of information management, vol. 35, no. 2, pp. 137-144, 2015.

J. Garnier, "Long-term changes in greenhouse gas emissions from French agriculture and livestock (1852–2014): From traditional agriculture to conventional intensive systems," Science of the Total Environment, vol. 660, pp. 1486-1501, 2019.

Y. Huang and M. Brown, "Advancing to the next generation of precision agriculture," Agriculture & food systems to, vol.

H. A. Issad, "A comprehensive review of Data Mining techniques in smart agriculture," Engineering in Agriculture, Environment and Food, vol. 12, no. 4, pp. 511-525, 2019.

H. Kendall, "Precision agriculture technology adoption: A qualitative study of small-scale commercial “family farms” located in the North China Plain," Precision Agriculture, pp. 1-33, 2022.

N. Khan, "Current progress and future prospects of agriculture technology: Gateway to sustainable agriculture," Sustainability, vol. 13, no. 9, pp. 4883, 2021.

C. Li and B. Niu, "Design of smart agriculture based on big data and Internet of things," International Journal of Distributed Sensor Networks, vol. 16, no. 5, p. 1550147720917065, 2020.

A. Monteiro, "Precision agriculture for crop and livestock farming—Brief review," Animals, vol. 11, no. 8, p. 2345, 2021.

S. Rajeswari, "A smart agricultural model by integrating IoT, mobile and cloud-based big data analytics," in 2017 international conference on intelligent computing and control (I2C2), 2017: IEEE, pp. 1-5.

C. Rega, "A classification of European agricultural land using an energy-based intensity indicator and detailed crop description," Landscape and urban planning, vol. 198, p. 103793, 2020.

V. Saiz-Rubio and F. Rovira-Más, "From smart farming towards agriculture 5.0: A review on crop data management," Agronomy, vol. 10, no. 2, p. 207, 2020.

Y. Su and X. Wang, "Innovation of agricultural economic management in the process of constructing smart agriculture by big data," Sustainable Computing: Informatics and Systems, vol. 31, p. 100579, 2021.

E. L. White, "Report from the conference, ‘identifying obstacles to applying big data in agriculture’," Precision Agriculture, vol. 22, pp. 306-315, 2021.

J. Xiao, "Coupling of agricultural product marketing and agricultural economic development based on big data analysis and “internet+”," Mobile Information Systems, vol. 2021, no. 1, p. 3702064, 2021.

Most read articles by the same author(s)

<< < 4 5 6 7 8 9 10 11 12 13 > >>

International Journal of Next-Generation Engineering and Technology

An Intelligent Systems-Based Evaluation Model of Rural Agricultural Development in China Inspired by International Precision Farming Technologies

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles