Pest identification via hyperspectral image and deep learning

Crops are attacked by a variety of pests and diseases during their growth. Different pests have different control measures, and being able to accurately identify pests has become the key to pest control. Traditional methods have relatively low accuracy in pest identification due to the complexity of their algorithms and their susceptibility to environmental interference. This paper proposes an end-to-end pest identification network that combines deep learning and hyperspectral imaging technology. This method can identify common pests for the purposes of effective pest control. Noise and redundant information in the hyperspectral image (HSI) spectral space are treated by one-dimensional convolution and the attention mechanism between spectral channels to design a spectral feature extraction module for the efficient use of spectral information. The three-dimensional convolution branch structure of different resolutions in parallel is used as the HSI feature extractor to secure rich spectral-spatial information. The output feature map maintains high resolution throughout its usage. To further enhance the feature extraction capabilities of the network, an adaptive spectral-spatial feature extraction module is inserted into each branch to dynamically weight different information, thereby reducing the HSI's undue influence. A hyperspectral imaging system was used to collect pest HSI, and a dataset containing nine kinds of common pests was constructed accordingly. The above method is used to test on this dataset, and the experimental results prove that this method has higher pest identification accuracy and is more suitable for pest identification tasks than other methods.

Automated summary

This paper proposes an end-to-end pest identification network that combines deep learning and hyperspectral imaging technology for effective pest control. The method utilizes spectral feature extraction and spectral-spatial feature extraction to enhance pest identification accuracy and proves to be more suitable for pest identification tasks than other methods.