Deep Cost-sensitive Learning for Wheat Frost Detection

TL;DR

This paper introduces a deep cost-sensitive learning approach using hyperspectral data and CNNs to improve wheat frost detection accuracy, especially for imbalanced datasets, achieving high overall and frost-specific detection rates.

Contribution

It proposes a novel cost-sensitive deep learning method with a CNN framework that effectively handles data imbalance in wheat frost detection using hyperspectral data.

Findings

Detection accuracy reached 0.943

Frost detection score was 0.623

Method improves frost sample detection rate

Abstract

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.