#Nn model sets budding series

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.

#Nn model sets budding series

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. It is exactly what it is supposed to be, a green tea lover's yellow tea.The flowering forecast provides recommendations for orchard cleaning, pest control, field management and fertilization, which can help increase tree vigor and resistance. The careful pick and unblemished pine needle shape make it perhaps the best looking of any of our yellows. It looks like a high quality hongqing green tea, tastes like one too, but still has the taro aroma which Rao thinks is essential. Dry fermentation, letting the tea it sit in a warm pile for a day, requires infinitely less labor than the traditional 'three smother three bake' wet yellowing. This light yellow tea is something Rao has created to satisfy the local green-oriented market. The leaves should be dark and the aroma similar to sweet taro. For him yellow tea should be something approaching the 2021 Orthodox Yellow Tea produced by the Huifeng Company.

He knew personally one of the two professors who helped revive yellow tea production in Huoshan while he was mayor in the late 1980's. The producer, a former mayor of Jinjishan who insists we call him 'Old Rao,' would be the first to tell you that this yellow tea is unorthodox. This is basically a standard Chinese baked green tea with a uniquely sweet, taro aroma. Tasting Notes: Taro Aroma Almond, Dairy, Grassy, Vanilla. Region: Dahuaping Town, Huoshan County, China Type: Yellow Tea, Light Yellowing, Dry Fermentation