Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision
Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To...
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MDPI AG
2025-06-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/12/1272 |
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| author | Wanna Fu Zhen Chen Qian Cheng Yafeng Li Weiguang Zhai Fan Ding Xiaohui Kuang Deshan Chen Fuyi Duan |
| author_facet | Wanna Fu Zhen Chen Qian Cheng Yafeng Li Weiguang Zhai Fan Ding Xiaohui Kuang Deshan Chen Fuyi Duan |
| author_sort | Wanna Fu |
| collection | DOAJ |
| description | Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome these difficulties, the study integrated computer vision techniques with UAV-based remote sensing data to establish a rapid and non-invasive method for estimating the LAI in maize. Multispectral imagery of maize was acquired via UAV platforms across various phenological stages, and vegetation features were derived based on the Excess Green (ExG) Index and the Hue–Saturation–Value (HSV) color space. LAI standardization was performed through edge detection and the cumulative distribution function. The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R<sup>2</sup> values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively. When HSV-based optimization was applied, VisLAI achieved even better performance, with R<sup>2</sup> values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages. The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). Among these, GB achieved the best performance, with R<sup>2</sup> values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34. However, VisLAI consistently outperformed all machine learning models, especially during the grain filling stage, demonstrating superior robustness and accuracy. The VisLAI model proposed in this study effectively utilizes UAV-captured visible light imagery and computer vision techniques to achieve accurate, efficient, and non-destructive estimation of maize LAI. It outperforms traditional and machine learning-based approaches and provides a reliable solution for real-world maize growth monitoring and agricultural decision-making. |
| format | Article |
| id | doaj-art-bb4cf47b9d994d3ebbcfaa6766047218 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-bb4cf47b9d994d3ebbcfaa67660472182025-08-20T03:30:25ZengMDPI AGAgriculture2077-04722025-06-011512127210.3390/agriculture15121272Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer VisionWanna Fu0Zhen Chen1Qian Cheng2Yafeng Li3Weiguang Zhai4Fan Ding5Xiaohui Kuang6Deshan Chen7Fuyi Duan8Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, ChinaInstitute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, ChinaInstitute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, ChinaInstitute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, ChinaInstitute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, ChinaInstitute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, ChinaInstitute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, ChinaInstitute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, ChinaInstitute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, ChinaPrecise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome these difficulties, the study integrated computer vision techniques with UAV-based remote sensing data to establish a rapid and non-invasive method for estimating the LAI in maize. Multispectral imagery of maize was acquired via UAV platforms across various phenological stages, and vegetation features were derived based on the Excess Green (ExG) Index and the Hue–Saturation–Value (HSV) color space. LAI standardization was performed through edge detection and the cumulative distribution function. The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R<sup>2</sup> values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively. When HSV-based optimization was applied, VisLAI achieved even better performance, with R<sup>2</sup> values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages. The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). Among these, GB achieved the best performance, with R<sup>2</sup> values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34. However, VisLAI consistently outperformed all machine learning models, especially during the grain filling stage, demonstrating superior robustness and accuracy. The VisLAI model proposed in this study effectively utilizes UAV-captured visible light imagery and computer vision techniques to achieve accurate, efficient, and non-destructive estimation of maize LAI. It outperforms traditional and machine learning-based approaches and provides a reliable solution for real-world maize growth monitoring and agricultural decision-making.https://www.mdpi.com/2077-0472/15/12/1272unmanned aerial vehiclevegetation indicesmachine learningcomputer visionleaf area index |
| spellingShingle | Wanna Fu Zhen Chen Qian Cheng Yafeng Li Weiguang Zhai Fan Ding Xiaohui Kuang Deshan Chen Fuyi Duan Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision Agriculture unmanned aerial vehicle vegetation indices machine learning computer vision leaf area index |
| title | Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision |
| title_full | Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision |
| title_fullStr | Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision |
| title_full_unstemmed | Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision |
| title_short | Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision |
| title_sort | maize leaf area index estimation based on machine learning algorithm and computer vision |
| topic | unmanned aerial vehicle vegetation indices machine learning computer vision leaf area index |
| url | https://www.mdpi.com/2077-0472/15/12/1272 |
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