Abstract
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.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, R., Sun, Y., Sun, Q. (2018). Automated Flowering Time Prediction Using Data Mining and Machine Learning. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-319-73447-7_56
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DOI: https://doi.org/10.1007/978-3-319-73447-7_56
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