Chlorophyll Content Estimation of Ginkgo Seedlings Based on Deep Learning and Hyperspectral Imagery
文献类型：期刊论文
| 作者 | Yue, Zilong1,2; Zhang, Qilin2; Zhu, Xingzhou2; Zhou, Kai2 |
| 刊名 | FORESTS
 |
| 出版日期 | 2024-11-01 |
| 卷号 | 15期号:11页码:2010 |
| 关键词 | Ginkgo seedlings hyperspectral imaging chlorophyll content 1D-CNN |
| DOI | 10.3390/f15112010 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Accurate estimation of chlorophyll content is essential for understanding the growth status and optimizing the cultivation practices of Ginkgo, a dominant multi-functional tree species in China. Traditional methods based on chemical analysis for determining chlorophyll content are labor-intensive and time-consuming, making them unsuitable for large-scale dynamic monitoring and high-throughput phenotyping. To accurately quantify chlorophyll content in Ginkgo seedlings under different nitrogen levels, this study employed a hyperspectral imaging camera to capture canopy hyperspectral images of seedlings throughout their annual growth periods. Reflectance derived from pure leaf pixels of Ginkgo seedlings was extracted to construct a set of spectral parameters, including original reflectance, logarithmic reflectance, and first derivative reflectance, along with spectral index combinations. A one-dimensional convolutional neural network (1D-CNN) model was then developed to estimate chlorophyll content, and its performance was compared with four common machine learning methods, including Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest (RF). The results demonstrated that the 1D-CNN model outperformed others with the first derivative spectra, achieving higher CV-R2 and lower RMSE values (CV-R2 = 0.80, RMSE = 3.4). Furthermore, incorporating spectral index combinations enhanced the model's performance, with the 1D-CNN model achieving the best performance (CV-R2 = 0.82, RMSE = 3.3). These findings highlight the potential of the 1D-CNN model in strengthening the chlorophyll estimations, providing strong technical support for the precise cultivation and the fertilization management of Ginkgo seedlings. |
| WOS关键词 | LEAF-AREA INDEX ; SPECTRAL REFLECTANCE ; VEGETATION INDEX ; INVERSION ; FLUORESCENCE ; PARAMETERS ; SYSTEM ; LEAVES ; MODEL |
| WOS研究方向 | Forestry |
| WOS记录号 | WOS:001366681600001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/210474]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhou, Kai |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Nanjing Forestry Univ, Coinnovat Ctr Sustainable Forestry Southern China, Nanjing 210037, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yue, Zilong,Zhang, Qilin,Zhu, Xingzhou,et al. Chlorophyll Content Estimation of Ginkgo Seedlings Based on Deep Learning and Hyperspectral Imagery[J]. FORESTS,2024,15(11):2010. |
| APA | Yue, Zilong,Zhang, Qilin,Zhu, Xingzhou,&Zhou, Kai.(2024).Chlorophyll Content Estimation of Ginkgo Seedlings Based on Deep Learning and Hyperspectral Imagery.FORESTS,15(11),2010. |
| MLA | Yue, Zilong,et al."Chlorophyll Content Estimation of Ginkgo Seedlings Based on Deep Learning and Hyperspectral Imagery".FORESTS 15.11(2024):2010. |
入库方式： OAI收割
来源：地理科学与资源研究所
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