Spectral Kolmogorov-Arnold Transformer for few-shot rice germplasm viability detection using hyperspectral imaging

Abstract

Rice is a fundamental staple crop germplasm and a vital resource for germplasm innovation, playing a critical role in global food security. Viability is a key indicator for evaluating the conservation and utilization of germplasm resources, ensuring high and stable grain yields. Viability loss during the germplasm conservation process is a natural-aging process. Given the large number of varieties and the rarity of certain samples, excessive destructive tests for viability assessment should be minimized and ultimately replaced by intelligent non-destructive detection methods. Therefore, it is imperative to explore intelligent non-destructive, few-shot, cross-variety/germplasm, and viability detection of rice germplasm based on natural-aging. Current algorithms for rice germplasm viability detection encounter significant challenges in achieving optimal performance under few-shot conditions. We propose a spectral Kolmogorov-Arnold Transformer algorithm, specifically designed for viability detection of rice germplasm under few-shot conditions. A feature enhancement module is implemented to improve the spectral feature representation capabilities of germplasm hyperspectral image (GHSI). A multi-scale spectral feature extraction module is designed to extract spectral features across multiple scales. A fusion of convolutional neural network and Transformer module is introduced to capture both global and local features of GHSI. Finally, a learnable activation function (Kolmogorov-Arnold networks, KAN) and global average pooling are employed for viability classification. Under the condition of 15 samples per class, the SKA-T achieved overall accuracies of 92.87%, 92.30%, and 92.57% for the three rice lines, respectively. These results demonstrate the effectiveness of SKA-T in intelligent non-destructive viability detection of rice germplasm under few-shot conditions.

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