Abstract
Plant phenotype is all physical, physiological, biochemical characteristics and traits that reflect the entire process of plant structural composition, growth and development. Timely phenotypic observation of plants is of great significance in terms of crop safety and environmental sustainability. Aiming at the problems of inaccurate flowering time prediction, time-consuming and waste of energy prediction in traditional pear tree phenotype observation, this paper studies the flowering prediction method of pear tree plants based on PCA-BP neural network. Taking pear tree as the research object, the meteorological observation data of Shijiazhuang Meteorological Station was analyzed by principal component analysis method, then three principal components with large correlation with pear flowering period were obtained. BP neural network model was introduced into the pear tree flowering period prediction and the error was reduced to one day, then.
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Gao, Q., Du, J., Su, J., Gilmore, A. (2022). Research on Pear Tree Flowering Period Prediction Method Based on Neural Network. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13338. Springer, Cham. https://doi.org/10.1007/978-3-031-06794-5_50
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DOI: https://doi.org/10.1007/978-3-031-06794-5_50
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