Hyperspectral imaging (HSI) technology has great potential for the efficient and accurate detection of plant diseases. To date, no studies have reported the identification of yellow vein clearing disease (YVCD) in lemon plants by using hyperspectral imaging. A major challenge in leveraging HSI for rapid disease diagnosis lies in efficiently processing high-dimensional data without compromising classification accuracy. In this study, hyperspectral feature extraction is optimized by introducing a novel hybrid 3D-2D-LcNet architecture combined with three-dimensional (3D) and two-dimensional (2D) convolutional layers-a methodological advancement over conventional single-mode CNNs. The competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were utilized to reduce the dimensionality of hyperspectral images and select the feature wavelengths for YVCD diagnosis. The spectra and hyperspectral images retrieved through feature wavelength selection were separately employed for the modeling process by using machine learning algorithms and convolutional neural network algorithms (CNN). Machine learning algorithms (such as support vector machine and partial least squares discriminant analysis) and convolutional neural network algorithms (CNN) (including 3D-ShuffleNetV2, 2D-LcNet and 2D-ShuffleNetV2) were utilized for comparison analysis. The results showed that CNN-based models have achieved an accuracy ranging from 93.90% to 97.35%, significantly outperforming machine learning approaches (ranging from 68.83% to 93.52%). Notably, the hybrid 3D-2D-LcNet has achieved the highest accuracy of 97.35% (CARS) and 96.86% (SPA), while reducing computational costs compared to 3D-CNNs. These findings suggest that hybrid 3D-2D-LcNet effectively balances computational complexity with feature extraction efficacy and robustness when handling spectral data of different wavelengths. Overall, this study offers insights into the rapidly processing hyperspectral images, thus presenting a promising method.