Estimating Wheat Chlorophyll Content Using a Multi-Source Deep Feature Neural Network
Chlorophyll plays a vital role in wheat growth and fertilization management. Accurate and efficient estimation of chlorophyll content is crucial for providing a scientific foundation for precision agricultural management. Unmanned aerial vehicles (UAVs), characterized by high flexibility, spatial re...
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MDPI AG
2025-07-01
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| Series: | Agriculture |
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| author | Jun Li Yali Sheng Weiqiang Wang Jikai Liu Xinwei Li |
| author_facet | Jun Li Yali Sheng Weiqiang Wang Jikai Liu Xinwei Li |
| author_sort | Jun Li |
| collection | DOAJ |
| description | Chlorophyll plays a vital role in wheat growth and fertilization management. Accurate and efficient estimation of chlorophyll content is crucial for providing a scientific foundation for precision agricultural management. Unmanned aerial vehicles (UAVs), characterized by high flexibility, spatial resolution, and operational efficiency, have emerged as effective tools for estimating chlorophyll content in wheat. Although multi-source data derived from UAV-based multispectral imagery have shown potential for wheat chlorophyll estimation, the importance of multi-source deep feature fusion has not been adequately addressed. Therefore, this study aims to estimate wheat chlorophyll content by integrating spectral and textural features extracted from UAV multispectral imagery, in conjunction with partial least squares regression (PLSR), random forest regression (RFR), deep neural network (DNN), and a novel multi-source deep feature neural network (MDFNN) proposed in this research. The results demonstrate the following: (1) Except for the RFR model, models based on texture features exhibit superior accuracy compared to those based on spectral features. Furthermore, the estimation accuracy achieved by fusing spectral and texture features is significantly greater than that obtained using a single type of data. (2) The MDFNN proposed in this study outperformed other models in chlorophyll content estimation, with an R<sup>2</sup> of 0.850, an RMSE of 5.602, and an RRMSE of 15.76%. Compared to the second-best model, the DNN (R<sup>2</sup> = 0.799, RMSE = 6.479, RRMSE = 18.23%), the MDFNN achieved a 6.4% increase in R<sup>2</sup>, and 13.5% reductions in both RMSE and RRMSE. (3) The MDFNN exhibited strong robustness and adaptability across varying years, wheat varieties, and nitrogen application levels. The findings of this study offer important insights into UAV-based remote sensing applications for estimating wheat chlorophyll under field conditions. |
| format | Article |
| id | doaj-art-d7bbcf4545944178aa222980508a80d7 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-d7bbcf4545944178aa222980508a80d72025-08-20T03:36:30ZengMDPI AGAgriculture2077-04722025-07-011515162410.3390/agriculture15151624Estimating Wheat Chlorophyll Content Using a Multi-Source Deep Feature Neural NetworkJun Li0Yali Sheng1Weiqiang Wang2Jikai Liu3Xinwei Li4College of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, ChinaChlorophyll plays a vital role in wheat growth and fertilization management. Accurate and efficient estimation of chlorophyll content is crucial for providing a scientific foundation for precision agricultural management. Unmanned aerial vehicles (UAVs), characterized by high flexibility, spatial resolution, and operational efficiency, have emerged as effective tools for estimating chlorophyll content in wheat. Although multi-source data derived from UAV-based multispectral imagery have shown potential for wheat chlorophyll estimation, the importance of multi-source deep feature fusion has not been adequately addressed. Therefore, this study aims to estimate wheat chlorophyll content by integrating spectral and textural features extracted from UAV multispectral imagery, in conjunction with partial least squares regression (PLSR), random forest regression (RFR), deep neural network (DNN), and a novel multi-source deep feature neural network (MDFNN) proposed in this research. The results demonstrate the following: (1) Except for the RFR model, models based on texture features exhibit superior accuracy compared to those based on spectral features. Furthermore, the estimation accuracy achieved by fusing spectral and texture features is significantly greater than that obtained using a single type of data. (2) The MDFNN proposed in this study outperformed other models in chlorophyll content estimation, with an R<sup>2</sup> of 0.850, an RMSE of 5.602, and an RRMSE of 15.76%. Compared to the second-best model, the DNN (R<sup>2</sup> = 0.799, RMSE = 6.479, RRMSE = 18.23%), the MDFNN achieved a 6.4% increase in R<sup>2</sup>, and 13.5% reductions in both RMSE and RRMSE. (3) The MDFNN exhibited strong robustness and adaptability across varying years, wheat varieties, and nitrogen application levels. The findings of this study offer important insights into UAV-based remote sensing applications for estimating wheat chlorophyll under field conditions.https://www.mdpi.com/2077-0472/15/15/1624remote sensingUAVchlorophyll contentwheatdeep learningdeep feature |
| spellingShingle | Jun Li Yali Sheng Weiqiang Wang Jikai Liu Xinwei Li Estimating Wheat Chlorophyll Content Using a Multi-Source Deep Feature Neural Network Agriculture remote sensing UAV chlorophyll content wheat deep learning deep feature |
| title | Estimating Wheat Chlorophyll Content Using a Multi-Source Deep Feature Neural Network |
| title_full | Estimating Wheat Chlorophyll Content Using a Multi-Source Deep Feature Neural Network |
| title_fullStr | Estimating Wheat Chlorophyll Content Using a Multi-Source Deep Feature Neural Network |
| title_full_unstemmed | Estimating Wheat Chlorophyll Content Using a Multi-Source Deep Feature Neural Network |
| title_short | Estimating Wheat Chlorophyll Content Using a Multi-Source Deep Feature Neural Network |
| title_sort | estimating wheat chlorophyll content using a multi source deep feature neural network |
| topic | remote sensing UAV chlorophyll content wheat deep learning deep feature |
| url | https://www.mdpi.com/2077-0472/15/15/1624 |
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