Spiking-LSTM: A novel hyperspectral image segmentation network for Sclerotinia detection

Abstract

Sclerotinia is a worldwide disease that often occurs at all growth stages of rapeseed, and can lead to 10 %∼70 % yield decline. It will also drastically reduce the oil content of seeds, which greatly increases the risk and difficulty of rapeseed cultivation. In order to address the problems of traditional chemical-based Sclerotinia detection methods such as complex operation, environmental pollution, plant damage and low efficiency, this study innovatively combined the two architectures of SNN (Spiking Neural Network) and LSTM, and proposed a spatial-spectral joint detection model Spiking-LSTM for the HSI segmentation. In the design process of the model, spiking neurons were used instead of gating functions in traditional LSTM units, while the back propagation of errors was solved by using gradient surrogate function. The experimental results show that when input data from the infected area, the neurons in hidden layer of the trained model exhibited distinctly regular spiking signals. Compared with the mainstream models, the Spiking-LSTM based on spatial-spectral data fusion has better performance in the evaluation parameters such as mAP, ClassAP, mIoU, FWIoU and Kappa coefficient. Its Sclerotinia detection mAP reached 94.3 % and was able to accurately extract the infected areas at the early-stage of infection. With essentially the same structure, the Spiking LSTM not only has higher detection accuracy but also, for the same HSI input, requires only one-fifth of the theoretical energy consumption compared to the traditional LSTM. This paper establishes the basis for the construction of large-scale SNN models, and also provides a reference for the application of SNNs in different fields.