Automated Flowering Time Prediction Using Data Mining and Machine Learning

Autor:	Runxuan Li, Qingquan Sun, Yu Sun
Rok vydání:	2018
Předmět:	0301 basic medicine business.industry computer.software_genre Machine learning Flowering time Field (computer science) 03 medical and health sciences 030104 developmental biology Day length Local environment Fraction (mathematics) Data mining Artificial intelligence business computer Mathematics
Zdroj:	Machine Learning and Intelligent Communications ISBN: 9783319734460 MLICOM (2)
DOI:	10.1007/978-3-319-73447-7_56
Popis:	This paper presents a solution for the predictions of flowering times concerning specific types of flowers. Since flower blooms are necessarily related to the local environment, the predictions (in months), are yielded by using machine learning to train a model considering the various environmental factors as variables. The environmental factors, which are temperature, precipitation, and the length of day, contribute to the chronological order of flowering periods. The predictions are accurate to a fraction of a month, and it can applied to control the flowering times by changing the values of the variables. The result provides an example of how data mining and machine learning presents itself to be a useful tool in the agricultural or environmental field.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::077a8da4d8c4ae8e1267a8c8eb86b6dc https://doi.org/10.1007/978-3-319-73447-7_56 Zobrazit plný text záznamu