Design of a Portable Nondestructive Instrument for Apple Watercore Grade Classification Based on 1DQCNN and Vis/NIR Spectroscopy.
Article
Wu, Haijian, Lin, Yong, Zhang, Wenbin et al. (2025). Design of a Portable Nondestructive Instrument for Apple Watercore Grade Classification Based on 1DQCNN and Vis/NIR Spectroscopy.
. Micromachines, 16(12), 1357. 10.3390/mi16121357
Wu, Haijian, Lin, Yong, Zhang, Wenbin et al. (2025). Design of a Portable Nondestructive Instrument for Apple Watercore Grade Classification Based on 1DQCNN and Vis/NIR Spectroscopy.
. Micromachines, 16(12), 1357. 10.3390/mi16121357
To address the challenge of nondestructively identifying watercore disease in apples during growth and maturation, a portable device was developed for real-time grading of apple watercore using visible/near-infrared (Vis/NIR) spectroscopy combined with a one-dimensional quadratic convolutional neural network (1DQCNN). The instrument enables rapid, nondestructive, and accurate detection of apple watercore grades. The AI-OX2000-13 micro-spectrometer is used as the core data acquisition unit, and an ARM processing system is built with the STM32F103VET6 as the main control chip. A 4G wireless communication module enables efficient and stable data transmission between the processor and computer, meeting the real-time detection needs of apple watercore content in orchard environments. To improve the scientific and accurate classification of watercore grades, this paper combines the BiSeNet and RIFE algorithms to construct a 3D model of apple watercore, allowing quantification of the degree of watercore and classification into four levels. Based on this, quadratic convolution operations are incorporated into a one-dimensional convolutional neural network (1DCNN), leading to the development of the 1D quadratic convolutional neural network (1DQCNN) model for watercore grade classification. Experimental results indicate that the model achieves a classification accuracy of 98.05%, outperforming traditional methods and conventional CNN models. The designed portable instrument demonstrates excellent accuracy and practicality in real-world applications.
