Nondestructive detection of apple watercore disease content based on 3D watercore model

Abstract

Current cultivation and research on Watercore apples lack precise evaluation methods and non-destructive detection techniques for Watercore content. In response, this study exploits the intrinsic distribution characteristics of Watercore and utilizes a RIFE interpolation-based feature slice stacking method to reconstruct a 3D model of individual Watercore—a task unattainable using conventional approaches. Employing the complete 3D Watercore model as a reference, the study further integrates near-infrared spectroscopy with the GAF-ConvNeXt algorithm to achieve five-class non-destructive detection of Watercore. Experimental results demonstrate that the MIoU between the RIFE-interpolated features and the original Watercore features attains a value of 0.826, thereby indicating high reliability. The reconstructed 3D models typically exhibit a central void, multiple uniformly distributed independent pillar-like structures along the periphery, and a greater volume in the upper half relative to the lower half. Furthermore, the five-class detection accuracy achieved using the GAF-ConvNeXt algorithm attains 98.10 %, thereby offering a more precise and scientifically robust method for the non-destructive evaluation of Watercore content in apples.