  As the demand for food surges and the agricultural sector undergoes a
transformative shift towards sustainability and efficiency, the need for
precise and proactive measures to ensure the health and welfare of livestock
becomes paramount. In the context of the broader agricultural landscape
outlined, the application of Hyperspectral Imaging (HSI) takes on profound
significance. HSI has emerged as a cutting-edge, non-destructive technique for
fast and accurate egg quality analysis, including the detection of chick embryo
mortality. However, the high cost and operational complexity compared to
conventional RGB imaging are significant bottlenecks in the widespread adoption
of HSI technology. To overcome these hurdles and unlock the full potential of
HSI, a promising solution is hyperspectral image reconstruction from standard
RGB images. This study aims to reconstruct hyperspectral images from RGB images
for non-destructive early prediction of chick embryo mortality. Firstly, the
performance of different image reconstruction algorithms, such as HRNET, MST++,
Restormer, and EDSR were compared to reconstruct the hyperspectral images of
the eggs in the early incubation period. Later, the reconstructed spectra were
used to differentiate live from dead chick-producing eggs using the XGBoost and
Random Forest classification methods. Among the reconstruction methods, HRNET
showed impressive reconstruction performance with MRAE of 0.0955, RMSE of
0.0159, and PSNR of 36.79 dB. This study motivated that harnessing imaging
technology integrated with smart sensors and data analytics has the potential
to improve automation, enhance biosecurity, and optimize resource management
towards sustainable agriculture 4.0.
