A Review: Comparative Analysis of Computer Vision Techniques for Defect Detection and Categorization in Bananas and Apples (Sebha University Conference Proceedings, 2024)
Detection and classification of these defects in bananas and apples using computer vision techniques are crucial for quality control, sorting processes, and ensuring consumer satisfaction. By accurately identifying and categorizing these defects, producers and retailers can take appropriate measures to minimize waste, maintain product quality, and enhance the overall marketability of fruits. This review offers a comprehensive summary of recent studies that have utilized computer vision techniques for the identification and categorization of defects in bananas and apples. It specifically investigates the distinctions between the two fruits in terms of the outcomes obtained from employing similar computer vision methods. The reviewed research highlights the effectiveness of various techniques, such as support vector machines, deep learning methods, and machine learning algorithms, in accurately detecting defects in both bananas and apples. By analyzing the results obtained from these techniques, the review aims to uncover any contrasting patterns or variations between the two fruits. Ultimately, this research provides valuable insights into the unique characteristics and challenges associated with defect detection in bananas and apples using computer vision methods.