Prediction of sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors

Prediction of sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors

Accurate pre-harvest prediction of sugar beet yield is vital for effective agricultural management and decision-making. However, traditional methods are constrained by reliance on empirical knowledge, time-consuming processes, resource intensiveness, and spatial-temporal variability in prediction ac...

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Bibliographic Details
Main Authors: Qing Wang, Ke Shao, Zhibo Cai, Yingpu Che, Haochong Chen, Shunfu Xiao, Ruili Wang, Yaling Liu, Baoguo Li, Yuntao Ma
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2025-06-01
Series:Artificial Intelligence in Agriculture
Subjects:
Sugar beet yield
Time-series data
Recurrent neural network
UAV
Meteorological factors
Online Access:http://www.sciencedirect.com/science/article/pii/S2589721725000236
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http://www.sciencedirect.com/science/article/pii/S2589721725000236

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