A Prediction Method of Seedling Transplanting Time with DCNN-LSTM Based on the Attention Mechanism
To improve the production efficiency and reduce the labor cost of seedling operations, cabbage was selected as the research subject, and a novel approach based on the attention mechanism combining the deep convolutional neural network (DCNN) and long short-term memory (LSTM) is proposed. First, the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/12/7/1504 |
| _version_ | 1797407899195015168 |
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| author | Huaji Zhu Chang Liu Huarui Wu |
| author_facet | Huaji Zhu Chang Liu Huarui Wu |
| author_sort | Huaji Zhu |
| collection | DOAJ |
| description | To improve the production efficiency and reduce the labor cost of seedling operations, cabbage was selected as the research subject, and a novel approach based on the attention mechanism combining the deep convolutional neural network (DCNN) and long short-term memory (LSTM) is proposed. First, the cabbage growth data and environmental monitoring data were normalized, and input samples were obtained by sliding the time window. Then, the DCNN and the LSTM were used to extract the spatial feature information and temporal correlation of the samples, respectively. At the same time, the attention mechanism was used to set the weight coefficients of different feature information and highlight the role of the main features of the sample in the model, thereby improving the prediction accuracy. By analyzing the experimental data collected by the Shandong Seedling Plant, the DCNN-LSTM method based on the proposed attention mechanism achieved good prediction results, providing experience for the engineering application of decision-making regarding seedling transplanting time. The experimental data showed that the mean absolute error, root-mean-square error, mean absolute percentage error, and symmetric mean absolute percentage error of the prediction results of this method were 0.356, 0.507, 0.157, and 0.082, respectively. Compared with the CNN, LSTM, LSTM-Attention and CNN-LSTM models, this model showed higher prediction accuracy. |
| first_indexed | 2024-03-09T03:49:25Z |
| format | Article |
| id | doaj.art-653fd5316e9c474fa150c2c02d3f4e53 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T03:49:25Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-653fd5316e9c474fa150c2c02d3f4e532023-12-03T14:29:49ZengMDPI AGAgronomy2073-43952022-06-01127150410.3390/agronomy12071504A Prediction Method of Seedling Transplanting Time with DCNN-LSTM Based on the Attention MechanismHuaji Zhu0Chang Liu1Huarui Wu2Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaTo improve the production efficiency and reduce the labor cost of seedling operations, cabbage was selected as the research subject, and a novel approach based on the attention mechanism combining the deep convolutional neural network (DCNN) and long short-term memory (LSTM) is proposed. First, the cabbage growth data and environmental monitoring data were normalized, and input samples were obtained by sliding the time window. Then, the DCNN and the LSTM were used to extract the spatial feature information and temporal correlation of the samples, respectively. At the same time, the attention mechanism was used to set the weight coefficients of different feature information and highlight the role of the main features of the sample in the model, thereby improving the prediction accuracy. By analyzing the experimental data collected by the Shandong Seedling Plant, the DCNN-LSTM method based on the proposed attention mechanism achieved good prediction results, providing experience for the engineering application of decision-making regarding seedling transplanting time. The experimental data showed that the mean absolute error, root-mean-square error, mean absolute percentage error, and symmetric mean absolute percentage error of the prediction results of this method were 0.356, 0.507, 0.157, and 0.082, respectively. Compared with the CNN, LSTM, LSTM-Attention and CNN-LSTM models, this model showed higher prediction accuracy.https://www.mdpi.com/2073-4395/12/7/1504transplanting timeprediction modeldeep convolutional neural networklong and short-term memory networkattention mechanism |
| spellingShingle | Huaji Zhu Chang Liu Huarui Wu A Prediction Method of Seedling Transplanting Time with DCNN-LSTM Based on the Attention Mechanism Agronomy transplanting time prediction model deep convolutional neural network long and short-term memory network attention mechanism |
| title | A Prediction Method of Seedling Transplanting Time with DCNN-LSTM Based on the Attention Mechanism |
| title_full | A Prediction Method of Seedling Transplanting Time with DCNN-LSTM Based on the Attention Mechanism |
| title_fullStr | A Prediction Method of Seedling Transplanting Time with DCNN-LSTM Based on the Attention Mechanism |
| title_full_unstemmed | A Prediction Method of Seedling Transplanting Time with DCNN-LSTM Based on the Attention Mechanism |
| title_short | A Prediction Method of Seedling Transplanting Time with DCNN-LSTM Based on the Attention Mechanism |
| title_sort | prediction method of seedling transplanting time with dcnn lstm based on the attention mechanism |
| topic | transplanting time prediction model deep convolutional neural network long and short-term memory network attention mechanism |
| url | https://www.mdpi.com/2073-4395/12/7/1504 |
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