Jina Hura and Joong Bae Ahn used the dynamically downscaled daily temperature to research the effect of global warming on the first-flowering data (FFD) of cherry, peach and pear in Northeast Asia. developed the prediction models of full blooming dates for the five peach cultivars at the six major peach cultivation sites using the DVR model, chill day model and new chill day models. Surgiura and Honjo founded that the DVR model accurately predicted the flowering dates with Root Mean Square Error (RMSE) of 1.23 days. The developmental rate (DVR) has been described as a functions of temperature, with the relationships between DVR and temperature being considered linear or exponential. Temperature-based models include the developmental rate (DVR) model, the chill day model and the new chill day model. When the accumulation of temperature reaches a certain degree Celsius, it is the initial flowering period of the plant. The method of the heat storage period is the same as the accumulated temperature method. Alcalá and Barranco determined the heat storage period based on 10-year phenology and temperature data, and used the heat accumulation threshold to predict flowering time. Temperature is a common meteorological factor of flowering and can influence a variety of stages in floral development. It is a mathematical expression used to simulate the plant growth and development process.Īccording to the number of meteorological elements, the flowering forecast model can be divided into single factor forecast model and multi-factor forecast model. The plant phenology model is based on the reaction mechanism of the plant growth and development process to the factors that constitute the climatic factors. During the growth and development of the plant in each year, with the seasonal changes of weather and climate, it changes from sprouting, branching and leafing, flowering and fruiting stages to deciduous dormancy and other phenological phenomena with regular changes. Climatic factors influence the phenology of many animal and plant species, rendering them susceptible to the effects of climate change. These meteorological elements not only provide basic materials and energy for organisms, but also constitute the external environmental conditions that are the growth and development of organisms, with determined yield and quality. Among the agrometeorological elements, the meteorological elements closely related to the production activities of crops are solar radiation, temperature, precipitation, humidity and wind et al. Climate change and crop management measures affect crop growth. Phenology is no longer a pastime of little scientific value. The change in the phenology of plants or animals reflects the response of living systems to climate change. The phenology is used to indicate the seasonal changes of the seasons, the response and adaptation process of the ecosystem to changes in the external environment. At the same time, the model solves the problem of rounding decimals in the prediction of flowering dates by the regression method.įlowering forecast is an important part in the construction of agro-meteorological index system and meteorological service system. The proposed model has high applicability and accuracy for flowering forecast. In the two-category task of flowering judgment, the idea of combining strategies in ensemble learning improves the effect of flowering judgment, and its AUC value increases from 0.81 and 0.80 of single model RF and AdaBoost to 0.82. The implementation shows that the proposed multivariable LSTM network has a good effect on the prediction of meteorological factors. Then, we evaluated the effectiveness of the model based on the number of error days and the number of days in advance. In this paper, by analyzing local meteorological data and phenological data of “Red Fuji” apples in Fen County, Linfen City, Shanxi Province, with the help of machine learning and neural networks, we proposed a method based on the combination of time series forecasting and classification forecasting is proposed to complete the dynamic forecasting model of local flowering in Ji County. Flowering forecast is not only an important part of the construction of agro-meteorological index system, but also an important part of the meteorological service system. The flowering forecast provides recommendations for orchard cleaning, pest control, field management and fertilization, which can help increase tree vigor and resistance.
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