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Chinese Agricultural Science Bulletin ›› 2025, Vol. 41 ›› Issue (14): 111-119.doi: 10.11924/j.issn.1000-6850.casb2024-0526

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Characteristics of Phenological Period and Prediction of Flowering Period in Korla Fragrant Pear

HUO Jin1(), ZAN Yongli2, DIAO Peng1, WU Xinguo2, WEI Yanying1, GONG Meiling1   

  1. 1 Bazhou Meteorological Bureau, Korla, Xinjiang 841000
    2 Korla Meteorological Bureau, Korla, Xinjiang 841000
  • Received:2024-08-14 Revised:2025-04-17 Online:2025-05-15 Published:2025-05-14

Abstract:

It is of great significance to study and forecast the phenological period of Korla fragrant pear in order to accurately grasp its growth cycle, optimize cultivation management, and improve yield and quality. The phenological evolution trend of Korla fragrant pear and the influence of main meteorological factors on the phenological period were analyzed by means of trend analysis, correlation analysis and significance test. Two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were used to predict the flowering period. The results showed that, in spring phenology, the peak stage of leaf development was delayed at a rate of 0.29 d/a, and the rest showed an advance trend, with an advance rate of 0.12-0.37 d/a, in which the advance of flower bud opening stage was the largest, and that of flowering peak stage was the least. In the autumn phenophase, the fruit ripening stage was advanced by 0.14 d/a, and the leaf discoloration stage and defoliation stage were delayed by 0.03-0.15 d/a. The growing season extended at a rate of 0.14 d/a, and the elongation was more obvious after 2012. Temperature is the main meteorological factor affecting the phenological period, and its influence on the spring phenological period is greater than that on the autumn phenological period. Sunshine hours and precipitation will also affect the phenological period. RF is better than LSTM in predicting the initial flowering period, and LSTM is better than RF in predicting the full flowering period. The research is expected to provide scientific basis for the optimization of cultivation management, quality and efficiency improvement, disaster prevention and mitigation of Korla fragrant pear.

Key words: Korla fragrant pear, phenophase, correlation analysis, random forest, long short-term memory, prediction of flowering period