Plasma rice yield prediction based on Bi-LSTM model

Yuan Wang; Wenhao Zhao; Xiaojiang Tang; Yang Liu; Hanyu Tang; Junwei Guo; Zhongqi Lin; Feng Huang

doi:10.1117/12.2674801

2 May 2023 Plasma rice yield prediction based on Bi-LSTM model

Yuan Wang, Wenhao Zhao, Xiaojiang Tang, Yang Liu, Hanyu Tang, Junwei Guo, Zhongqi Lin, Feng Huang

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126420J (2023) https://doi.org/10.1117/12.2674801
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

Rice is one of the most important grains in the world and its yield increase and quality improvement have always been the focus of research. Low temperature plasma (LTP) technology is a green agricultural technology, which can increase crop yield and improve crop quality. Accurate yield prediction and evaluation can promote the adjustment of agricultural production structure, the integration of agricultural resources and the healthy development of agricultural industry. It can also help to adjust crop management and commercial decisions (for example, to determine sales prices and marketing plans). In this paper, a plasma rice yield prediction model based on Bi-directional Long Short-Term Memory (Bi-LSTM) artificial neural network is constructed, which can accurately predict plasma rice yield. Compared with Multiple Linear Regression (MLR) and Support Vector Machine (SVM) methods, the results showed that the Bi-LSTM prediction model could well predict plasma rice yield, and the average error of predicted yield was 25 kg per mu (1mu = 666.67m²).

Citation Download Citation

Yuan Wang, Wenhao Zhao, Xiaojiang Tang, Yang Liu, Hanyu Tang, Junwei Guo, Zhongqi Lin, and Feng Huang "Plasma rice yield prediction based on Bi-LSTM model", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126420J (2 May 2023); https://doi.org/10.1117/12.2674801

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