冬小麦
产量(工程)
环境科学
遥感
人工智能
计算机科学
气象学
农学
地质学
材料科学
地理
冶金
生物
作者
Wenzheng Ye,Tinghuai Ma,Zilong Jin,Huan Rong,Benjamin Kwapong Osibo,M. M. Abdel Wahab,Yu-Ming Su,Bright Bediako-Kyeremeh
标识
DOI:10.1117/1.jrs.18.014507
摘要
Timely and accurate prediction of winter wheat yield contributes to ensuring national food security. We propose a CNN- bidirectional gated recurrent unit method with triple attention for winter wheat yield prediction, named CBTA. This deep learning model uses convolutional neural networks to mine the spatial spectral information in hyperspectral remote sensing images. Furthermore, the bidirectional gated recurrent unit is used to adaptively learn the time dependence between the various stages of winter wheat growth. Data from Henan Province, China, is used in this study to train the model and also verify its prediction performance and stability. The results from our experiment show that our proposed model has an excellent effect on yield prediction in the county, with root-mean-square-error, mean absolute error, and R2 of 0.469 t/ha, 0.336 t/ha, and 0.827, respectively. Moreover, our findings suggested that the precision of our model using the data from sowing to heading-flowering stage was very close to that from sowing to ripening stage, which proves that the CBTA model can accurately predict the yield of winter wheat 1 to 2 months in advance.
科研通智能强力驱动
Strongly Powered by AbleSci AI