ABSTRACT Precise and fast estimation of 1,8‐cineole levels in large cardamom is essential for proper quality assessment and improving value chain efficiency in the essential oil sector. This work introduces an adaptive‐band convolutional transformer neural network (AdBand‐CTNet) designed to forecast 1,8‐cineole content using near‐infrared (NIR) spectral measurements. The approach incorporates a differentiable adaptive band selection (ABS) module combined with an attention scheme, allowing the model to learn the most informative wavelength zones and the regression parameters simultaneously in a fully integrated training process. In contrast to traditional feature selection strategies or fixed‐width spectral partitioning, the proposed architecture dynamically optimizes both the center and span of every band through gradient updates, producing concise and interpretable spectral features. Samples from seven cultivars of large cardamom collected across multiple agroclimatic areas of West Bengal and Sikkim, India, were studied. Reference values for 1,8‐cineole were determined using gas chromatography–mass spectrometry (GC–MS), and NIR spectra were captured between 900 and 1700 nm. Over 100 randomized trials, the AdBand‐CTNet reached a mean absolute error of 0.0633, a root mean squared error of 0.1301, and an R 2 of 0.9969 on the test data, demonstrating its outstanding precision and robustness. The introduced method removes unnecessary wavelength segments while still maintaining excellent prediction capability. Its flexible and explainable architecture makes it well‐suited for industrial scenarios that require trustworthy and accurate chemical composition assessment, offering a more economical option compared with standard chromatographic methods.
Rahaman et al. (Fri,) studied this question.