  Frost damage is one of the main factors leading to wheat yield reduction.
Therefore, the detection of wheat frost accurately and efficiently is
beneficial for growers to take corresponding measures in time to reduce
economic loss. To detect the wheat frost, in this paper we create a
hyperspectral wheat frost data set by collecting the data characterized by
temperature, wheat yield, and hyperspectral information provided by the
handheld hyperspectral spectrometer. However, due to the imbalance of data,
that is, the number of healthy samples is much higher than the number of frost
damage samples, a deep learning algorithm tends to predict biasedly towards the
healthy samples resulting in model overfitting of the healthy samples.
Therefore, we propose a method based on deep cost-sensitive learning, which
uses a one-dimensional convolutional neural network as the basic framework and
incorporates cost-sensitive learning with fixed factors and adjustment factors
into the loss function to train the network. Meanwhile, the accuracy and score
are used as evaluation metrics. Experimental results show that the detection
accuracy and the score reached 0.943 and 0.623 respectively, this demonstration
shows that this method not only ensures the overall accuracy but also
effectively improves the detection rate of frost samples.
