Low-Carbon Jujube Moisture Content Detection Based on Spectral Selection and Reconstruction

In recent years, the combination of hyperspectral imagery and deep learning has been widely used in agricultural Artificial Intelligence of Things (AIoT), such as agricultural product quality assessment and crop disease detection. However, this often comes at the cost of substantial computational power and energy consumption. In this paper, we focused on data-efficient green computing for low-carbon jujube moisture content detection. First, in order to compress the hyperspectral images capacity, a spectral selection algorithm based on swarm intelligence was proposed to screen necessary and sensitive spectral dimensions. Then, a spectral reconstruction model was established to realize the selected hyperspectral bands reconstruction from RGB image, aiming to reduce the high cost of hyperspectral imaging. Finally, a model based on the fusion of spectral data and reconstructed image was constructed to realize efficient jujube moisture content detection. The experimental results show that the 10 feature bands screened by the proposed selection method can adequately characterize the water information of jujube, and the proposed reconstruction model outperforms other works with the MRAE of 0.1635. The carbon emissions of our proposed reconstruction model are significantly lower than other methods. Further, the spectral-image fusion model achieves satisfactory detection result of jujube moisture content, with the RMSE of 0.0082. In summary, the proposed spectral selection, reconstruction, and detection methods can achieve high precision while reducing carbon emissions, which have important guidance for low-carbon and sustainable agricultural AIoT applications.