Paper Info
Title
Predicting Grain Losses and Waste Rate Along the Entire Chain: A Multitask Multigated Recurrent Unit Autoencoder Based Method
Abstract
Predicting grain losses and waste rate (LWR) is critical for agricultural planning and grain policy development. Capturing the stage interaction and generating robust features are the main challenges in grain LWR prediction. In this article, we propose MTGA, a Multitask Gated recurrent unit (GRU) Autoencoder, approach to 1) obtain the robust feature representation for the prediction task and 2) explore the time-ordered interactions among different stages of the grain chain. Specifically, we design multiple GRU encoder-decoder pairs to co-reconstruct the stage features in a common space for robust feature learning. Then, an attention mechanism is proposed better to fuse the reconstructed features from the GRU encoder-decoder pairs. Furthermore, we utilize the multitask for reconstructed loss and grain LWR prediction. We introduce the reconstructed loss task as an auxiliary task to help us to represent the robust features. Besides, we introduce the LWR prediction as main task to learn the parameters for prediction task. We collected the data with questionnaires, interviews, or data from grain management institutes for experiments. The evaluation results show that grain LWR prediction by our approach achieves the best results compared to several state-of-the-art prediction models. Moreover, our method gains overall performance decline of 12.5-18.3% on mean absolute error and root mean square error metrics.
Year
DOI
Venue
2021
10.1109/TII.2020.3030709
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Deep learning,grain losses and waste rate (LWR) prediction,multitask prediction,recurrent skip connection network (RSCN)
Journal
17
Issue
ISSN
Citations 
6
1551-3203
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jie Cao162773.36
Youquan Wang2575.72
Jing He354.44
Weichao Liang401.35
Haicheng Tao5132.91
Guixiang Zhu621.73