Open Access

ARTICLE

Research on Forecasting Flowering Phase of Pear Tree Based on Neural Network

Zhenzhou Wang¹, Yinuo Ma¹, Pingping Yu^1,*, Ning Cao², Heiner Dintera³

1 School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050000, China
2 School of Internet of Things and Software Technology, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China
3 German-Russian Institute of Advanced Technologies, Karan, 420126, Russia

* Corresponding Author: Pingping Yu. Email:

Computers, Materials & Continua 2021, 68(3), 3431-3446. https://doi.org/10.32604/cmc.2021.017729

Received 03 February 2021; Accepted 08 March 2021; Issue published 06 May 2021

Abstract

Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes. The current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience, which have problems such as inaccurate recognition time, time-consuming and energy sapping. Therefore, this paper proposes a neural network-based method for predicting the flowering phase of pear tree. Firstly, based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019, three principal components (the temperature factor, weather factor, and humidity factor) with high correlation coefficient with the flowering phase of pear tree are obtained by using the principal component analysis method. Then, the three components are used as input factors for the BP neural network. A BP neural network prediction model is constructed based on genetic algorithm optimization. The crossover operator and mutation operator in the adaptive genetic algorithm are improved. Finally, the meteorological sample data from 2013 to 2019 are used to test and verify the algorithm in this paper. The results demonstrate that, the model can solve the local optimization problem of the BP neural network model. The prediction results of the flowering phase of pear tree are evaluated in terms of relevance and prediction accuracy. Both are superior to the traditional effective accumulated temperature and the prediction results of the stepwise regression method. This method can provide more reliable forecast information for the blooming period, which can provide decision-making reference for improving the development of tourism industry.

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