石庆兰,束金阳,李道亮,黄凯欣,查海涅.基于BiLSTM-GRU融合网络的稻虾养殖溶解氧含量预测[J].农业机械学报,2023,54(10):364-370.
SHI Qinglan,SHU Jinyang,LI Daoliang,HUANG Kaixin,ZHA Hainie.Dissolved Oxygen Prediction in Rice and Shrimp Culture Based on BiLSTM-GRU Fusion Neural Networks[J].Transactions of the Chinese Society for Agricultural Machinery,2023,54(10):364-370.
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| 基于BiLSTM-GRU融合网络的稻虾养殖溶解氧含量预测 [下载全文] |
| Dissolved Oxygen Prediction in Rice and Shrimp Culture Based on BiLSTM-GRU Fusion Neural Networks [Download Pdf][in English] |
| 投稿时间：2023-03-31 |
| DOI：10.6041/j.issn.1000-1298.2023.10.036 |
| 中文关键词: 稻虾共作 溶解氧 预测模型 融合循环神经网络 |
| 基金项目:山东省重点研发计划项目（2021TZXD006） |
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| 中文摘要:在稻虾养殖模式中溶解氧含量（浓度）是养殖水体的重要指标之一，其直接影响小龙虾的摄食量和新陈代谢，因此在养殖过程中精准预测溶解氧含量至关重要。针对稻虾养殖中溶解氧含量变化复杂，难以快速准确预测的问题，提出了BiLSTM-GRU融合神经网络预测模型。为了保证精准预测，首先对传感器进行了清洗校准，并根据偏移量对历史数据进行了修正。在此基础上构建了基于BiLSTM和GRU的融合神经网络训练模型，BiLSTM提取更多特征因子，GRU实现快速预测，快速准确预测溶解氧含量变化。为了使监测预测性能更优，对不同采样周期下的资源损耗及预测模型性能进行综合对比分析，确定了传感器数据最优采样周期为30min。进一步与LSTM、GRU、BiLSTM以及BiGRU模型对比，表明本文提出的BiLSTM-GRU融合神经网络模型的预测效果更好，其平均绝对误差、均方根误差和决定系数分别为0.2759mg/L、0.6160mg/L和0.9547，比传统的LSTM神经网络模型分别高25.14%、13.25%和2.22%。 |
| SHI Qinglan SHU Jinyang LI Daoliang HUANG Kaixin ZHA Hainie |
| China Agricultural University; Anqing Normal University |
| Key Words:rice-prawn farming dissolved oxygen prediction model fusion cyclic neural network |
| Abstract:Dissolved oxygen is an essential parameter for monitoring water quality in rice-prawn farming, as it plays a significant role in crayfish feeding and metabolism. Accurately predicting dissolved oxygen content is critical for maintaining optimal farming conditions and preventing environmental damage. However, dissolved oxygen levels can be challenging to predict due to the complexity of the factors affecting them. A BiLSTM-GRU fusion neural network prediction model that can overcome these challenges was proposed. The model combined the benefits of BiLSTM, which extracted more feature factors, and GRU, which achieved fast and accurate prediction. The sensors and corrected historical data were cleaned and calibrated based on the offset to ensure accuracy. A comprehensive analysis of the resource consumption and prediction performance of the model under different sampling periods was conducted and it was determined that 30 minutes was the optimal sampling period. The proposed model was compared with traditional LSTM, GRU, BiLSTM, and BiGRU models, which was found that the model was demonstrated better prediction performance, with mean absolute error, root mean square error, and determination coefficient of 0.2759mg/L, 0.6160mg/L, and 0.9547, respectively. These values were 25.14%, 13.25%, and 2.22% higher than those of the traditional LSTM neural network model. Overall, the proposed BiLSTM-GRU fusion neural network prediction model had significant potential for improving the accuracy of dissolved oxygen content prediction in rice prawn farming. |
Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.
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