Handling Experimental Data Outliers in the Efficient Cultivation of Dendrobium Officinale for Digital Agriculture
Year: 2025
Type: conference-paper
Abstract: The digital economy is changing quickly at its peak and the use of big data in farming has become a key approach to support agricultural modernization. The application approaches and technical paths of big data technology in the efficient cultivation of a new variety of Dendrobium officinale, "Minghu No. 4," and the processing of outliers of digital cultivation during experimental data are discussed in the paper. The analytical process includes modeling the IoT data collection, big data analysis, and the design of smart decision support systems, which enable authors to define the strategies to drop the natural outliers in the experiments during the digital cultivation, thereby offering technical assistance for improving agricultural production management through big data technology.
Authors: YingJun Zhou, Xiulian Lin, Zeheng Zhan
Institution: Huizhou Engineering Vocational College
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4412789961