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.

中文翻译：

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近红外光谱多任务深度学习改进谷物品质性状预测

使用近红外 (NIR) 光谱开发了蛋白质、水分和谷物类型的全局预测模型。该模型是一个深度卷积神经网络，使用从小麦、大麦、豌豆和扁豆全谷物中捕获的 NIR 光谱数据进行训练。深度学习模型执行多任务学习以同时预测谷物蛋白质、水分和类型，与线性方法（例如偏最小二乘回归）相比，预测误差显着减少。此外，结果表明，卷积网络架构比相同大小的简单前馈神经网络架构更有效地学习。因此，除了提高准确性之外，所提出的深度网络在模型开发时间和所需的计算资源方面都非常有效。