A Pattern Recognition Approach to Broccoli Maturity Assessment Using Image Processing and Deep Learning

This study presents a promising deep learning-based framework for automatic classification of broccoli maturity stages through various image processing analyses. By leveraging advanced image processing techniques and various deep learning models, the proposed system accurately segments broccoli heads from complex backgrounds, achieving a segmentation accuracy of 98.52%, even under challenging visual conditions such as shadows and partial occlusion by leaves. The subsequent maturity classification phase reaches a training accuracy of 99% and a testing accuracy of 91%, demonstrating strong generalization capabilities. A key strength of the proposed method lies in its resilience to suboptimal image quality, making it suitable for deployment in uncontrolled, real-world environments. Extensive experimental evaluations confirm the effectiveness of the framework, highlighting its potential as a feasible solution for object segmentation and stage classification tasks in pattern recognition scenarios.