PENDETEKSIAN KEMATANGAN MANGGA BERBASIS FITUR ANALISIS WARNA DENGAN METODE CNN

Abstract

Abstract
Accurate detection of mango ripeness is crucial in the agricultural industry to ensure optimal fruit quality.
Manual inspection by employees is often inconsistent and time-consuming. Therefore, this study proposes
the use of Convolutional Neural Network (CNN) and OpenCV techniques to determine the ripeness level of
mangoes based on skin color analysis. CNN is utilized to detect visual patterns in mango images and classify
ripeness, while OpenCV plays a role in image preprocessing, such as color space conversion and feature
extraction. This system is developed using the Python programming language. Based on the research results,
this method can accurately detect mango ripeness, making it suitable for industrial-scale applications to
enhance the efficiency and accuracy of automated fruit sorting.