Prediction of maize growth stages based on deep learning

•ConvLSTM encoder-decoder model can forecast daily weather factors correctly.•Hybrid model and data-driven model can predict maize growth stages.•Data-driven model outperforms hybrid model in forecasting performance.•The proposed techniques can be used for the arrangement of agricultural activities....

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Veröffentlicht in:	Computers and electronics in agriculture Jg. 172; S. 105351
Hauptverfasser:	Yue, Yang, Li, Jin-Hai, Fan, Li-Feng, Zhang, Li-Li, Zhao, Peng-Fei, Zhou, Qiao, Wang, Nan, Wang, Zhong-Yi, Huang, Lan, Dong, Xue-Hui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.05.2020 Elsevier BV
Schlagworte:	Artificial neural networks Climate Coders ConvLSTM encoder-decoder model Corn Crop growth Crop yield crops Daily weather forecast data collection Data-driven model Deep learning developmental stages empirical models Encoders-Decoders harvest date Hybrid model Maize growth stages Mathematical models mechanical harvesting Meteorological data Neural networks planting Precipitation prediction solar radiation Sunlight temperature Tillage Volatility Weather forecasting Hybrid model Daily weather forecast Data-driven model ConvLSTM encoder-decoder model Maize growth stages
ISSN:	0168-1699, 1872-7107
Online-Zugang:	Volltext
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