Monitoring the Maize Canopy Chlorophyll Content Using Discrete Wavelet Transform Combined with RGB Feature Fusion
To evaluate the accuracy of Discrete Wavelet Transform (DWT) in monitoring the chlorophyll (CHL) content of maize canopies based on RGB images, a field experiment was conducted in 2023. Images of maize canopies during the jointing, tasseling, and grouting stages were captured using unmanned aerial v...
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2025-01-01
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| Series: | Agronomy |
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| author | Wenfeng Li Kun Pan Yue Huang Guodong Fu Wenrong Liu Jizhong He Weihua Xiao Yi Fu Jin Guo |
| author_facet | Wenfeng Li Kun Pan Yue Huang Guodong Fu Wenrong Liu Jizhong He Weihua Xiao Yi Fu Jin Guo |
| author_sort | Wenfeng Li |
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| description | To evaluate the accuracy of Discrete Wavelet Transform (DWT) in monitoring the chlorophyll (CHL) content of maize canopies based on RGB images, a field experiment was conducted in 2023. Images of maize canopies during the jointing, tasseling, and grouting stages were captured using unmanned aerial vehicle (UAV) remote sensing to extract color, texture, and wavelet features and to construct a color and texture feature dataset and a fusion of wavelet, color, and texture feature datasets. Backpropagation neural network (BP), Stacked Ensemble Learning (SEL), and Gradient Boosting Decision Tree (GBDT) models were employed to develop CHL monitoring models for the maize canopy. The performance of these models was evaluated by comparing their predictions with measured CHL data. The results indicate that the dataset integrating wavelet features achieved higher monitoring accuracy compared to the color and texture feature dataset. Specifically, for the integrated dataset, the BP model achieved an R<sup>2</sup> value of 0.728, an RMSE of 3.911, and an NRMSE of 15.24%; the SEL model achieved an R<sup>2</sup> value of 0.792, an RMSE of 3.319, and an NRMSE of 15.34%; and the GBDT model achieved an R<sup>2</sup> value of 0.756, an RMSE of 3.730, and an NRMSE of 15.45%. Among these, the SEL model exhibited the highest monitoring accuracy. This study provides a fast and reliable method for monitoring maize growth in field conditions. Future research could incorporate cross-validation with hyperspectral and thermal infrared sensors to further enhance model reliability and expand its applicability. |
| format | Article |
| id | doaj-art-39c82685f506427dbdf1603a4ff975bb |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Agronomy |
| spelling | doaj-art-39c82685f506427dbdf1603a4ff975bb2025-01-24T13:17:12ZengMDPI AGAgronomy2073-43952025-01-0115121210.3390/agronomy15010212Monitoring the Maize Canopy Chlorophyll Content Using Discrete Wavelet Transform Combined with RGB Feature FusionWenfeng Li0Kun Pan1Yue Huang2Guodong Fu3Wenrong Liu4Jizhong He5Weihua Xiao6Yi Fu7Jin Guo8Yunnan International Joint Laboratory of Crop Smart Production, Yunnan Agricultural University, Kunming 650231, ChinaYunnan International Joint Laboratory of Crop Smart Production, Yunnan Agricultural University, Kunming 650231, ChinaYunnan International Joint Laboratory of Crop Smart Production, Yunnan Agricultural University, Kunming 650231, ChinaYunnan International Joint Laboratory of Crop Smart Production, Yunnan Agricultural University, Kunming 650231, ChinaYunnan International Joint Laboratory of Crop Smart Production, Yunnan Agricultural University, Kunming 650231, ChinaYunnan International Joint Laboratory of Crop Smart Production, Yunnan Agricultural University, Kunming 650231, ChinaDehong Agricultural Technology Extension Centre, Dehong 678499, ChinaDehong Agricultural Technology Extension Centre, Dehong 678499, ChinaYunnan Agri-Environmental Protection and Monitoring Station, Kunming 650201, ChinaTo evaluate the accuracy of Discrete Wavelet Transform (DWT) in monitoring the chlorophyll (CHL) content of maize canopies based on RGB images, a field experiment was conducted in 2023. Images of maize canopies during the jointing, tasseling, and grouting stages were captured using unmanned aerial vehicle (UAV) remote sensing to extract color, texture, and wavelet features and to construct a color and texture feature dataset and a fusion of wavelet, color, and texture feature datasets. Backpropagation neural network (BP), Stacked Ensemble Learning (SEL), and Gradient Boosting Decision Tree (GBDT) models were employed to develop CHL monitoring models for the maize canopy. The performance of these models was evaluated by comparing their predictions with measured CHL data. The results indicate that the dataset integrating wavelet features achieved higher monitoring accuracy compared to the color and texture feature dataset. Specifically, for the integrated dataset, the BP model achieved an R<sup>2</sup> value of 0.728, an RMSE of 3.911, and an NRMSE of 15.24%; the SEL model achieved an R<sup>2</sup> value of 0.792, an RMSE of 3.319, and an NRMSE of 15.34%; and the GBDT model achieved an R<sup>2</sup> value of 0.756, an RMSE of 3.730, and an NRMSE of 15.45%. Among these, the SEL model exhibited the highest monitoring accuracy. This study provides a fast and reliable method for monitoring maize growth in field conditions. Future research could incorporate cross-validation with hyperspectral and thermal infrared sensors to further enhance model reliability and expand its applicability.https://www.mdpi.com/2073-4395/15/1/212maize (<i>Zea mays</i> L.)machine learningchlorophyllDWTfeature fusion |
| spellingShingle | Wenfeng Li Kun Pan Yue Huang Guodong Fu Wenrong Liu Jizhong He Weihua Xiao Yi Fu Jin Guo Monitoring the Maize Canopy Chlorophyll Content Using Discrete Wavelet Transform Combined with RGB Feature Fusion Agronomy maize (<i>Zea mays</i> L.) machine learning chlorophyll DWT feature fusion |
| title | Monitoring the Maize Canopy Chlorophyll Content Using Discrete Wavelet Transform Combined with RGB Feature Fusion |
| title_full | Monitoring the Maize Canopy Chlorophyll Content Using Discrete Wavelet Transform Combined with RGB Feature Fusion |
| title_fullStr | Monitoring the Maize Canopy Chlorophyll Content Using Discrete Wavelet Transform Combined with RGB Feature Fusion |
| title_full_unstemmed | Monitoring the Maize Canopy Chlorophyll Content Using Discrete Wavelet Transform Combined with RGB Feature Fusion |
| title_short | Monitoring the Maize Canopy Chlorophyll Content Using Discrete Wavelet Transform Combined with RGB Feature Fusion |
| title_sort | monitoring the maize canopy chlorophyll content using discrete wavelet transform combined with rgb feature fusion |
| topic | maize (<i>Zea mays</i> L.) machine learning chlorophyll DWT feature fusion |
| url | https://www.mdpi.com/2073-4395/15/1/212 |
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