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• W4378602519 abstract "The quantum yield (QY) values of crops are important for assessing photosynthetic efficiency. Our hypothesis is that high-throughput prediction models of QY values can be established based on vegetation indices (VIs). The objectives of this research were to predict QY values based on VIs with high correlations and to compare the performances of classical machine learning with that of B-NAS methods. Compared with support vector regression (SVR) and other traditional prediction methods, in this study, a novel prediction model based on an optimised neural architecture search (B-NAS) was proposed, and achieved comparatively better results. In addition, an improved architecture was obtained and compared for ten different search architectures on the experimental dataset. The experimental results showed that the neural network model constructed based on the B-NAS method predicted QY values outperformed the models obtained using SVR and Adaboost methods in terms of all considered performance metrics (R2, RMSE, RPD, Bias). Meanwhile, the proposed model meets the criteria for a class A model. The final B-NAS model was tested on an independent validation set, obtaining similar performance metrics and an accuracy of 92% for post-prediction classification into three equal interval QY classes. An analysis of the neural architecture obtained by B-NAS showed that a four-layer network architecture had certain advantages. As well, the importance of the preprocessing and dropout layers is discussed." @default.
• W4378602519 created "2023-05-29" @default.
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• W4378602519 date "2023-06-01" @default.
• W4378602519 modified "2023-09-23" @default.
• W4378602519 title "Using an optimised neural architecture search for predicting the quantum yield of photosynthesis of winter wheat" @default.
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• W4378602519 doi "https://doi.org/10.1016/j.biosystemseng.2023.04.015" @default.
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