Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation

Research summary

Combines near-infrared spectroscopy and deep learning to estimate chickpea flour quality and composition without destructive testing.

Approach and contribution

Uses 136 chickpea varieties to compare CNNs, vision transformers and graph convolutional networks, with the best-performing CNN compared against partial least squares regression.

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