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Tea Plantation Frost Damage Early Warning Using a Two-Fold Method for temperature prediction

Phyton-International Journal of Experimental Botany 2022年第10期91卷 2269-2282页

作者： Zhengyu Wu Kaiqiang Li Lin Yuan Jingcheng Zhang Xianfeng Zhou Dongmei Chen Kaihua Wei College of Artificial Intelligence Hangzhou Dianzi UniversityHangzhou310018China School of Information Engineering and Art and Design Zhejiang University of Water Resources and Electric PowerHangzhou310018China

As the source and main producing area of tea in the world, China has formed unique tea culture, and achievedremarkable economic benefits. However, frequent meteorological disasters, particularly low temperature frostdamage in late spring has seriously threatened the growth status of tea trees and caused quality and yield reduction of tea industry. Thus, timely and accurate early warning of frost damage occurrence in specific tea garden isvery important for tea plantation management and economic values. Aiming at the problems existing in currentmeteorological disaster forecasting methods, such as difficulty in obtaining massive meteorological data, largeamount of calculation for predicted models and incomplete information on frost damage occurrence, this paperproposed a two-fold algorithm for short-term and real-time prediction of temperature using field environmentaldata, and temperature trend results from a nearest local weather station for accurate frost damage occurrence leveldetermination, so as to achieve a specific tea garden frost damage occurrence prediction in a microclimate. Timeseries meteorological data collected from a small weather station was used for testing and parameterization of atwo-fold method, and another dataset acquired from Tea Experimental Base of Zhejiang University was furtherused to validate the capability of a two-fold model for frost damage forecasting. Results showed that comparedwith the results of autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR),the proposed two-fold method using a second order Furrier fitting model and a K-Nearest Neighbor model(K = 3) with three days historical temperature data exhibited excellent accuracy for frost damage occurrence prediction on consideration of both model accuracy and computation (98.46% forecasted duration of frost damage,and 95.38% for forecasted temperature at the onset time). For field test in a tea garden, the proposed methodaccurately predicted thre

关键词： short-term temperature prediction Fourier fitting K-Nearest Neighbor frost damage tea plantation

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