Sistem Cerdas Berbasis Image Processing dan deep learning untuk Deteksi Lapisan Lilin pada Permukaan Buah

Abstract

Grapes are among the most perishable agricultural commodities. To improve shelf life and visual appeal, artificial wax coatings are often applied to their surface. However, these coatings may pose health risks if not properly detected. This study aims to develop an intelligent system based on image processing and deep learning to automatically and non-destructively detect the presence of wax coatings on apples. The dataset consists of a total of 312 apple images, collected using smartphone and digital cameras and expanded through data augmentation to increase variation and training volume. Classification was carried out using a Convolutional Neural Network architecture with input images resized to 150x150 pixels and trained for 20 epochs using the ImageDataGenerator library. The resulting model achieved a training accuracy of up to 99.36% and a validation accuracy of 100%. Testing confirmed that the system can effectively distinguish between waxed and unwaxed apple surfaces by recognizing differences in texture and light reflection. This system shows strong potential for implementation in automated post-harvest quality control within agricultural industries.