Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learning
Abstract Background Rice is one of the major food crops in the world, and the monitoring of its growth condition is of great significance for guaranteeing food security and promoting sustainable agricultural development. Leaf area index (LAI) is a key indicator for assessing the growth condition and...
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BMC
2025-05-01
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| Series: | Plant Methods |
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| Online Access: | https://doi.org/10.1186/s13007-025-01398-1 |
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| author | Haixia Li Qian Li Chunlai Yu Shanjun Luo |
| author_facet | Haixia Li Qian Li Chunlai Yu Shanjun Luo |
| author_sort | Haixia Li |
| collection | DOAJ |
| description | Abstract Background Rice is one of the major food crops in the world, and the monitoring of its growth condition is of great significance for guaranteeing food security and promoting sustainable agricultural development. Leaf area index (LAI) is a key indicator for assessing the growth condition and yield potential of rice, and the traditional methods for obtaining LAI have problems such as low efficiency and large error. With the development of remote sensing technology, unmanned aerial multispectral remote sensing combined with deep learning technology provides a new way for efficient and accurate estimation of LAI in rice. Results In this study, a multispectral camera mounted on a UAV was utilized to acquire rice canopy image data, and rice LAI was uniformly estimated over multiple periods by the multilayer perceptron (MLP) and convolutional neural network (CNN) models in deep learning. The results showed that the CNN model based on five-band reflectance images (490, 550, 670, 720, and 850 nm) as input after feature screening exhibited high estimation accuracy at different growth stages. Compared with the traditional MLP model with multiple vegetation indices as inputs, the CNN model could better process the original multispectral image data, effectively avoiding the problem of vegetation index saturation, and improving the accuracies by 4.89, 5.76, 10.96, 1.84 and 6.01% in the rice tillering, jointing, booting, and heading periods, respectively, and the overall accuracy was improved by 6.01%. Moreover, the model accuracies (MLP and CNN) before and after variable screening showed noticeable changes. Conducting variable screening contributed to a substantial improvement in the accuracy of rice LAI estimation. Conclusions UAV multispectral remote sensing combined with CNN technology provides an efficient and accurate method for the unified multi-period estimation of rice LAI. Moreover, the generalization ability and adaptability of the model were further improved by rational variable screening and data enhancement techniques. This study can provide a technical support for precision agriculture and a more accurate solution for rice growth monitoring. More feature extraction and variable screening methods can be further explored in future studies by optimizing the model structure to improve the accuracy and stability of the model. |
| format | Article |
| id | doaj-art-9aa2fd2273ec4c5a8646ac54f6d6e869 |
| institution | DOAJ |
| issn | 1746-4811 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj-art-9aa2fd2273ec4c5a8646ac54f6d6e8692025-08-20T03:16:47ZengBMCPlant Methods1746-48112025-05-0121111610.1186/s13007-025-01398-1Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learningHaixia Li0Qian Li1Chunlai Yu2Shanjun Luo3Huanghe University of Science and TechnologyAerospace Information Research Institute, Henan Academy of SciencesHuanghe University of Science and TechnologyAerospace Information Research Institute, Henan Academy of SciencesAbstract Background Rice is one of the major food crops in the world, and the monitoring of its growth condition is of great significance for guaranteeing food security and promoting sustainable agricultural development. Leaf area index (LAI) is a key indicator for assessing the growth condition and yield potential of rice, and the traditional methods for obtaining LAI have problems such as low efficiency and large error. With the development of remote sensing technology, unmanned aerial multispectral remote sensing combined with deep learning technology provides a new way for efficient and accurate estimation of LAI in rice. Results In this study, a multispectral camera mounted on a UAV was utilized to acquire rice canopy image data, and rice LAI was uniformly estimated over multiple periods by the multilayer perceptron (MLP) and convolutional neural network (CNN) models in deep learning. The results showed that the CNN model based on five-band reflectance images (490, 550, 670, 720, and 850 nm) as input after feature screening exhibited high estimation accuracy at different growth stages. Compared with the traditional MLP model with multiple vegetation indices as inputs, the CNN model could better process the original multispectral image data, effectively avoiding the problem of vegetation index saturation, and improving the accuracies by 4.89, 5.76, 10.96, 1.84 and 6.01% in the rice tillering, jointing, booting, and heading periods, respectively, and the overall accuracy was improved by 6.01%. Moreover, the model accuracies (MLP and CNN) before and after variable screening showed noticeable changes. Conducting variable screening contributed to a substantial improvement in the accuracy of rice LAI estimation. Conclusions UAV multispectral remote sensing combined with CNN technology provides an efficient and accurate method for the unified multi-period estimation of rice LAI. Moreover, the generalization ability and adaptability of the model were further improved by rational variable screening and data enhancement techniques. This study can provide a technical support for precision agriculture and a more accurate solution for rice growth monitoring. More feature extraction and variable screening methods can be further explored in future studies by optimizing the model structure to improve the accuracy and stability of the model.https://doi.org/10.1186/s13007-025-01398-1Leaf area indexPrecision agricultureDronesMultispectral imageryRice |
| spellingShingle | Haixia Li Qian Li Chunlai Yu Shanjun Luo Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learning Plant Methods Leaf area index Precision agriculture Drones Multispectral imagery Rice |
| title | Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learning |
| title_full | Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learning |
| title_fullStr | Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learning |
| title_full_unstemmed | Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learning |
| title_short | Unified estimation of rice canopy leaf area index over multiple periods based on UAV multispectral imagery and deep learning |
| title_sort | unified estimation of rice canopy leaf area index over multiple periods based on uav multispectral imagery and deep learning |
| topic | Leaf area index Precision agriculture Drones Multispectral imagery Rice |
| url | https://doi.org/10.1186/s13007-025-01398-1 |
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