Feature Recognition and Modeling Analysis of Apple Images Based on Yolov8X-Seg Training Model

Abstract

To address the inefficiencies and high costs inherent in traditional apple harvesting methods that rely on manual labor, this study aims to develop an automated detection system capable of accurate apple quantity recognition and maturity assessment under complex orchard conditions. By integrating advanced instance segmentation with agricultural automation technology, we seek to establish a foundational framework for intelligent harvesting robots. The proposed methodology leverages the Yolov8X-seg model enhanced through image sharpening and median filtering preprocessing, which optimizes edge feature extraction while suppressing environmental noise. The Adam gradient descent algorithm is systematically applied to refine model parameters, enabling multi-scale feature capture through convolutional-pooling layer combinations and precise classification via fully connected layers. Experimental validations demonstrate that our optimized framework achieves a 12.7% improvement in detection accuracy and 28.4% faster inference speed compared to baseline models, effectively overcoming occlusion and overlapping fruit challenges. These advancements not only verify the model's capability in maturity differentiation through spectral analysis but also reveal its potential for real-time monitoring applications. The research outcomes provide critical technical support for intelligent orchard management systems, marking a significant step toward reducing agricultural labor dependency and advancing precision farming practices.