Guo Congcong, Liu Liantao, Sun Hongchun, Wang Nan, Zhang Ke, Zhang Yongjiang, Zhu Jijie, Li Anchang, Bai Zhiying, Liu Xiaoqing, Dong Hezhong, Li Cundong
State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China.
Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang, China.
Front Plant Sci. 2022 Oct 18;13:1007150. doi: 10.3389/fpls.2022.1007150. eCollection 2022.
The chlorophyll fluorescence parameter 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. is a drought tolerance index reflecting the best drought tolerant cotton genotype. Therefore, 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 . The Savitzky-Golay + 1D-CNN model had the best robustness and accuracy (RMSE = 0.016, MAE = 0.009, MAPE = 0.011). In addition, the 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.
叶绿素荧光参数在植物非生物胁迫中具有重要意义。当前的采集方法必须处理植物的暗适应问题，无法实现快速、实时和高通量测量。然而，基于高光谱模型识别对不同基因型增加投入验证了其处理大量和可变样本的能力。是反映最佳耐旱棉花基因型的耐旱指数。因此，对不同棉花品种的高光谱预测以及耐旱性评估值得探索。本研究对80个棉花品种进行了研究。在正常和干旱胁迫条件下，于开花期、结铃期和吐絮期获取棉花高光谱数据。接下来，使用对一维卷积神经网络（1D-CNN）、分类提升（CatBoost）、轻梯度提升机（LightBGM）、极端梯度提升（XGBoost）、决策树（DT）、随机森林（RF）、梯度提升决策树（GBDT）、自适应提升（AdaBoost）、额外树（ET）和K近邻（KNN）进行建模。Savitzky-Golay + 1D-CNN模型具有最佳的稳健性和准确性（均方根误差=0.016，平均绝对误差=0.009，平均绝对百分比误差=0.011）。此外，预测的耐旱系数与人工测量的耐旱系数相似。因此，可利用高光谱全波段技术建立1D-CNN模型来监测不同耐旱程度的棉花品种。该技术在评估棉花干旱状况时具有无损、快速且准确 的特点，有助于推动智能规模农业发展。
