S2NN: Spectra-spiking neural network for energy-efficient wheat protein estimation using hyperspectral imaging

• First application of SNN for HSI regression analysis. • Spectra-Spiking Neural Network (S 2 NN) is proposed for wheat protein estimation. • The model built for protein content ranges from 9.5 % to 17.25 % by weight. • The S 2 NN reduces approximately 15 to 108 times energy consumption and uses approximately 5 to 34 times fewer FLOPs compared to ANN, CNN, and Transformer. Hyperspectral imaging (HSI) combined with traditional machine learning techniques has demonstrated an accurate assessment of seed chemical composition. However, their high computational demands and energy consumption make them unsuitable for real-time applications. Spiking neural networks (SNNs) are recognized as an energy-efficient alternative due to their spike-based mechanism. We explore SNN in the field of HSI for the regression analysis to estimate the wheat protein content. We proposed a spectra-spiking neural network (S 2 NN) over a single timestep that uses direct input encoding to analyze spectral data more efficiently. S 2 NN achieved an R² value of 0.9969, RMSE, and RPD of 0.0813 and 17.85, respectively. The S 2 NN was compared with other neural network models (i.e., ANN, CNN, and Transformer) in terms of accuracy, training time, floating-point operations (FLOPs), and energy consumption. We first compared the performance of the S 2 NN with ANN while maintaining a similar network architecture for both models. In this comparison, S 2 NN achieved a 5.08 % higher R² value and was approximately 2.06 times more energy-efficient. Next, we train the ANN, CNN, and Transformer to reach their maximum predictive accuracy, yielding results comparable to those of S 2 NN. In this case, the S 2 NN reduces energy consumption by approximately 15 to 108 times, requires approximately 5 to 34 times fewer FLOPs, and demonstrates significantly shorter training times compared to other neural network architectures. These findings show the potential of S 2 NNs as an efficient alternative to other neural networks, particularly for resource-constrained real-time agricultural applications where high-end computational devices may not be available. The code is available at https://github.com/Apurva-Git-Hub/Spectra-SNN.git .