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Predicting F-v/F-m and evaluating cotton drought tolerance using hyperspectral and 1D-CNN

文献类型： 外文期刊

作者： Guo, Congcong 1 ; Liu, Liantao 1 ; Sun, Hongchun 1 ; Wang, Nan 1 ; Zhang, Ke 1 ; Zhang, Yongjiang 1 ; Zhu, Jijie 3 ; Li, Anchang 1 ; Bai, Zhiying 1 ; Liu, Xiaoqing 1 ; Dong, Hezhong 4 ; Li, Cundong 1 ;

作者机构： 1.Hebei Agr Univ, Coll Agron, State Key Lab North China Crop Improvement & Regul, Key Lab Crop Growth Regulat Hebei Prov, Baoding, Peoples R China

2.Hebei Agr Univ, Coll Mech & Elect Engn, Baoding, Hebei, Peoples R China

3.Hebei Acad Agr & Forestry Sci, Inst Cereal & Oil Crops, Shijiazhuang, Peoples R China

4.Shandong Acad Agr Sci, Cotton Res Ctr, Shandong Key Lab Cotton Culture & Physiol, Jinan, Peoples R China

关键词： chlorophyll fluorescence parameter F-v; F-m; high-throughput measurement; cotton; drought tolerance; hyperspectral; one-dimensional convolutional neural network

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： The chlorophyll fluorescence parameter F-v/F-m is significant in abiotic plant stress. Current acquisition methods must deal with the dark adaptation of plants, which cannot achieve rapid, real-time, and high-throughput measurements. However, increased inputs on different genotypes based on hyperspectral model recognition verified its capabilities of handling large and variable samples. F-v/F-m is a drought tolerance index reflecting the best drought tolerant cotton genotype. Therefore, F-v/F-m hyperspectral prediction of different cotton varieties, and drought tolerance evaluation, are worth exploring. In this study, 80 cotton varieties were studied. The hyperspectral cotton data were obtained during the flowering, boll setting, and boll opening stages under normal and drought stress conditions. Next, One-dimensional convolutional neural networks (1D-CNN), Categorical Boosting (CatBoost), Light Gradient Boosting Machines (LightBGM), eXtreme Gradient Boosting (XGBoost), Decision Trees (DT), Random Forests (RF), Gradient elevation decision trees (GBDT), Adaptive Boosting (AdaBoost), Extra Trees (ET), and K-Nearest Neighbors (KNN) were modeled with F-v/F-m. The Savitzky-Golay + 1D-CNN model had the best robustness and accuracy (RMSE = 0.016, MAE = 0.009, MAPE = 0.011). In addition, the F-v/F-m prediction drought tolerance coefficient and the manually measured drought tolerance coefficient were similar. Therefore, cotton varieties with different drought tolerance degrees can be monitored using hyperspectral full band technology to establish a 1D-CNN model. This technique is non-destructive, fast and accurate in assessing the drought status of cotton, which promotes smart-scale agriculture.

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