Technical gazette, Vol. 31 No. 2, 2024.
Original scientific paper
https://doi.org/10.17559/TV-20231107001080
Formation Mechanism and Implementation Path of a Digital Agriculture Innovation Ecosystem
Yongxiang He
; School of Urban Design, Wuhan University, Wuhan, 430072, China; Hubei Habitat Environment Research Centre of Engineering and Technology, Wuhan, 430072, China
Jinghua Song
; School of Urban Design, Wuhan University, Wuhan, 430072, China; Hubei Habitat Environment Research Centre of Engineering and Technology, Wuhan, 430072, China
Wenjun Ouyang
; School of economics and management, China University of Geosciences (Wuhan), Wuhan, 430074, China
Qinghua Li
; College of Economics and Management, Yantai Nanshan University, Yantai, 265713, China
Abstract
Digital agricultural innovation ecosystems defined the notion of agricultural innovation ecosystems in regional areas. Developing a data economy for agriculture based on digital spaces necessitates an awareness of and proficiency with digital innovation ecosystems. The digital formation of agriculture has played a great role in enhancing agrarian production, encouraging the ecological development of the agricultural economy, and accomplishing sustainable economic goals. The profound integration of the digital economy and the agriculture industry has become a major concern. A multifaceted technology expansion across the agricultural economy, a Remote Sensing Assisted Digital Agriculture Innovation Ecosystem (RS-DAIE) has been developed to enhance country-level digital agriculture requirements. Therefore, simple guidelines for building an efficient marketing strategy are crucial for expanding access to healthy food options and fostering the growth of organic farmers locally and internationally. The trial findings show that RS-DAIE has the finest accuracy by 98.9%, reliability rate by 99.3%, data transmission by 97.3%, and moisture content ratios, which are better than other technologies.
Keywords
artificial intelligence; digital agriculture; innovation ecosystems; machine learning; remote sensing
Hrčak ID:
314828
URI
Publication date:
29.2.2024.
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