Transfer Learning For Fruit Detection

Abstract

In recent years, increasing awareness of healthy diets has led to a growing demand for high-quality and fresh fruits, making accurate fruit detection and freshness assessment essential in agriculture and food supply chains. However, traditional manual inspection methods are time-consuming, labor-intensive, and prone to human error, especially when handling large volumes of produce. To address this problem, this study proposes an automated image-based fruit detection approach using transfer learning. The method employs a pre-trained VGG19 convolutional neural network for feature extraction, followed by a Softmax classifier to categorize fruits such as apples, bananas, and oranges based on freshness. Image preprocessing techniques including resizing and normalization are applied to enhance model robustness. Experimental results demonstrate that the VGG19-based model achieves high classification accuracy and outperforms several existing approaches, confirming its effectiveness in fruit detection tasks. In conclusion, the proposed system provides a reliable and efficient solution for automated fruit freshness classification and shows strong potential for real-world applications in smart agriculture, quality inspection, and retail automation.