Detection of Rotten Fruits at Pomona Fruit House Using the Convolutional Neural Network Method

Kosasih, Ferly
Hendri
Hendrik, Jackri

Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Vol. 5 No. 1 (2025): October 2025

The growing public awareness of healthy lifestyles has led to an increasing demand for fresh and high-quality fruits. However, during storage and distribution, fruits are prone to spoilage due to environmental and biological factors. The manual identification process of spoiled fruits remains limited in terms of accuracy and efficiency. To address this issue, this study proposes the application of digital image processing technology based on Convolutional Neural Network (CNN) to automatically detect the condition of fruits. This system is designed to assist in quality monitoring at locations such as Rumah Buah Pomona by classifying fresh and spoiled fruits based on their visual features. This solution is expected to improve the effectiveness of fruit distribution and reduce potential losses caused by unfit products.