Corrigendum to “Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes” [Agric. Water Manage. 189 (2017) 98–110]

Elsayed, Salah; Elhoweity, Mohamed; Ibrahim, Hazem H.; Dewir, Yaser Hassan; Migdadi, Hussein M.; Schmidhalter, Urs

doi:10.1016/j.agwat.2019.105873

Cited by 1 publication

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“… Rasooli Sharabian et al., (2014) reported similar results. Elsayed et al. (2020) also revealed that compared to a single spectral index, a PLSR model based on spectral index data fusion and canopy temperature improved the GY prediction accuracy of barley and wheat under water stress.…”

Section: Discussionmentioning

confidence: 94%

Predicting Fv/Fm and evaluating cotton drought tolerance using hyperspectral and 1D-CNN

et al. 2022

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The chlorophyll fluorescence parameter Fv/Fm 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. Fv/Fm is a drought tolerance index reflecting the best drought tolerant cotton genotype. Therefore, Fv/Fm 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 Fv/Fm. The Savitzky-Golay + 1D-CNN model had the best robustness and accuracy (RMSE = 0.016, MAE = 0.009, MAPE = 0.011). In addition, the Fv/Fm 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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Section: Discussionmentioning

confidence: 94%