Multimodal Deep Learning Models in Precision Agriculture: Cotton Yield Prediction Based on Unmanned Aerial Vehicle Imagery and Meteorological Data
This study investigates a multimodal deep learning framework that integrates unmanned aerial vehicle (UAV) multispectral imagery with meteorological data to predict cotton yield. The study analyzes the impact of different neural network architectures, including the CNN feature extraction layer, the...
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MDPI AG
2025-05-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/5/1217 |
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| author | Chunbo Jiang Xiaoshuai Guo Yongfu Li Ning Lai Lei Peng Qinglong Geng |
| author_facet | Chunbo Jiang Xiaoshuai Guo Yongfu Li Ning Lai Lei Peng Qinglong Geng |
| author_sort | Chunbo Jiang |
| collection | DOAJ |
| description | This study investigates a multimodal deep learning framework that integrates unmanned aerial vehicle (UAV) multispectral imagery with meteorological data to predict cotton yield. The study analyzes the impact of different neural network architectures, including the CNN feature extraction layer, the depth of the fully connected layer, and the method of integrating meteorological data, on model performance. Experimental results show that the model combining UAV multispectral imagery with weekly meteorological data achieved optimal yield prediction accuracy (RMSE = 0.27 t/ha; R<sup>2</sup> = 0.61). Specifically, models based on AlexNet (Model 9) and CNN2conv (Model 18) exhibited superior accuracy. ANOVA results revealed that deeper fully connected layers significantly reduced RMSE, while variations in CNN architectural complexity had no statistically significant effect. Furthermore, although the models exhibited comparable prediction accuracy (RMSE: 0.27–0.33 t/ha; R<sup>2</sup>: 0.61–0.69 across test datasets), their yield prediction spatial distributions varied significantly (e.g., Model 9 predicted a mean yield of 3.88 t/ha with a range of 2.51–4.89 t/ha, versus Model 18 at 3.74 t/ha and 2.33–4.76 t/ha), suggesting the need for further evaluation of spatial stability. This study underscores the potential of deep learning models integrating UAV and meteorological data for precision agriculture, offering valuable insights for optimizing spatiotemporal data integration strategies in future research. |
| format | Article |
| id | doaj-art-1acc1d49679e45e0bbf26d78a3be3bdc |
| institution | DOAJ |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Agronomy |
| spelling | doaj-art-1acc1d49679e45e0bbf26d78a3be3bdc2025-08-20T03:14:39ZengMDPI AGAgronomy2073-43952025-05-01155121710.3390/agronomy15051217Multimodal Deep Learning Models in Precision Agriculture: Cotton Yield Prediction Based on Unmanned Aerial Vehicle Imagery and Meteorological DataChunbo Jiang0Xiaoshuai Guo1Yongfu Li2Ning Lai3Lei Peng4Qinglong Geng5Agricultural Engineering and Information Technology, College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, ChinaAgricultural Engineering and Information Technology, College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, ChinaXinjiang Academy of Agricultural Sciences, Resource and Environmental Information Technology Innovation Team, Urumqi 830091, ChinaXinjiang Academy of Agricultural Sciences, Resource and Environmental Information Technology Innovation Team, Urumqi 830091, ChinaXinjiang Academy of Agricultural Sciences, Resource and Environmental Information Technology Innovation Team, Urumqi 830091, ChinaXinjiang Academy of Agricultural Sciences, Resource and Environmental Information Technology Innovation Team, Urumqi 830091, ChinaThis study investigates a multimodal deep learning framework that integrates unmanned aerial vehicle (UAV) multispectral imagery with meteorological data to predict cotton yield. The study analyzes the impact of different neural network architectures, including the CNN feature extraction layer, the depth of the fully connected layer, and the method of integrating meteorological data, on model performance. Experimental results show that the model combining UAV multispectral imagery with weekly meteorological data achieved optimal yield prediction accuracy (RMSE = 0.27 t/ha; R<sup>2</sup> = 0.61). Specifically, models based on AlexNet (Model 9) and CNN2conv (Model 18) exhibited superior accuracy. ANOVA results revealed that deeper fully connected layers significantly reduced RMSE, while variations in CNN architectural complexity had no statistically significant effect. Furthermore, although the models exhibited comparable prediction accuracy (RMSE: 0.27–0.33 t/ha; R<sup>2</sup>: 0.61–0.69 across test datasets), their yield prediction spatial distributions varied significantly (e.g., Model 9 predicted a mean yield of 3.88 t/ha with a range of 2.51–4.89 t/ha, versus Model 18 at 3.74 t/ha and 2.33–4.76 t/ha), suggesting the need for further evaluation of spatial stability. This study underscores the potential of deep learning models integrating UAV and meteorological data for precision agriculture, offering valuable insights for optimizing spatiotemporal data integration strategies in future research.https://www.mdpi.com/2073-4395/15/5/1217UAV multispectral imagerymeteorological data integrationcottonmultimodal deep learningprecision agriculture |
| spellingShingle | Chunbo Jiang Xiaoshuai Guo Yongfu Li Ning Lai Lei Peng Qinglong Geng Multimodal Deep Learning Models in Precision Agriculture: Cotton Yield Prediction Based on Unmanned Aerial Vehicle Imagery and Meteorological Data Agronomy UAV multispectral imagery meteorological data integration cotton multimodal deep learning precision agriculture |
| title | Multimodal Deep Learning Models in Precision Agriculture: Cotton Yield Prediction Based on Unmanned Aerial Vehicle Imagery and Meteorological Data |
| title_full | Multimodal Deep Learning Models in Precision Agriculture: Cotton Yield Prediction Based on Unmanned Aerial Vehicle Imagery and Meteorological Data |
| title_fullStr | Multimodal Deep Learning Models in Precision Agriculture: Cotton Yield Prediction Based on Unmanned Aerial Vehicle Imagery and Meteorological Data |
| title_full_unstemmed | Multimodal Deep Learning Models in Precision Agriculture: Cotton Yield Prediction Based on Unmanned Aerial Vehicle Imagery and Meteorological Data |
| title_short | Multimodal Deep Learning Models in Precision Agriculture: Cotton Yield Prediction Based on Unmanned Aerial Vehicle Imagery and Meteorological Data |
| title_sort | multimodal deep learning models in precision agriculture cotton yield prediction based on unmanned aerial vehicle imagery and meteorological data |
| topic | UAV multispectral imagery meteorological data integration cotton multimodal deep learning precision agriculture |
| url | https://www.mdpi.com/2073-4395/15/5/1217 |
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