Multi-task deep learning of near infrared spectra for improved grain quality trait predictions
| Autor: | S Assadzadeh, Pankaj Maharjan, Linda S. McDonald, Joe Panozzo, C.K. Walker |
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| Rok vydání: | 2020 |
| Předmět: |
Network architecture
Artificial neural network business.industry Deep learning 010401 analytical chemistry Multi-task learning 02 engineering and technology 01 natural sciences Convolutional neural network 0104 chemical sciences Partial least squares regression 0202 electrical engineering electronic engineering information engineering Grain quality Feedforward neural network 020201 artificial intelligence & image processing Artificial intelligence business Biological system Spectroscopy |
| Zdroj: | Journal of Near Infrared Spectroscopy. 28:275-286 |
| ISSN: | 1751-6552 0967-0335 |
| DOI: | 10.1177/0967033520939318 |
| Popis: | A global predictive model was developed for protein, moisture, and grain type, using near infrared (NIR) spectra. The model is a deep convolutional neural network, trained on NIR spectral data captured from wheat, barley, field pea, and lentil whole grains. The deep learning model performs multi-task learning to simultaneously predict grain protein, moisture, and type, with a significant reduction in prediction errors compared to linear approaches (e.g., partial least squares regression). Moreover, it is shown that the convolutional network architecture learns much more efficiently than simple feedforward neural network architectures of the same size. Thus, in addition to improved accuracy, the presented deep network is very efficient to implement, both in terms of model development time, and the required computational resources. |
| Databáze: | OpenAIRE |
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