文献类型： 外文期刊

作者： Cui, Jintao ¹ ; Sawut, Mamat ¹ ; Ailijiang, Nuerla ⁴ ; Manlike, Asiya ⁵ ; Hu, Xin ¹ ;

作者机构： 1.Xinjiang Univ, Coll Geog & Remote Sensing Sci, Urumqi 830017, Peoples R China

2.Xinjiang Univ, Xinjiang Key Lab Oasis Ecol, Urumqi 830017, Peoples R China

3.Xinjiang Univ, Higher Educ Inst, Key Lab Smart City & Environm Modelling, Urumqi 830017, Peoples R China

4.Xinjiang Univ, Coll Ecol & Environm, Key Lab Oasis Ecol, Educ Minist, Urumqi 830017, Peoples R China

5.Xinjiang Acad Anim Sci, Grassland Res Inst, Urumqi 830057, Peoples R China

6.Xinjiang Acad Anim Sci, Field Orientat Observat & Res Stn Grassland Ecol E, Urumqi 830057, Peoples R China

关键词： reflectance; water leaf content; pre-processing; CNN

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 8 期

页码：

收录情况： SCI

摘要： Water scarcity is one of the most significant environmental factors that inhibits photosynthesis and decreases the growth and productivity of plants. Using the deep learning convolutional neural network (CNN) model, this study evaluates the ability of spectroscopy to estimate leaf water content (LWC) in fruit trees. During midday, spectral data were acquired from leaf samples obtained from three distinct varieties of fruit trees, encompassing the spectral range spanning from 350 to 2500 nm. Then, for spectral preprocessing, the fractional order derivative (FOD) and continuous wavelet transform (CWT) algorithms were used to reduce the effects of scattering and noise on the collected spectra. Finally, the CNN model was developed to predict LWC in different fruit trees. The results showed that: (1) The spectra treated with CWT and FOD could improve the spectrum expression ability by improving the correlation between spectra and LWC. The correlation level of FOD treatment was higher than that of CWT treatment. (2) The CNN model was developed using FOD (1.2), and CWT (3) performed better than other traditional machine learning methods, such as RFR, SVR, and PLSR. (3) Further validation using additional samples demonstrated that the CNN model had good stability and quantitative prediction capability for the LWC of fruit trees (R-2 > 0.95, root mean square error (RMSE) < 1.773%, and relative percentage difference (RPD) > 4.26). The results may provide an effective way to predict fruit LWC using a CNN-based model.