Automated Apple Wax Detection with Convolutional Neural Networks

In this work, the application of wax coatings on fruits is a widespread practice aimed at enhancing visual appeal and shelf life but raises concerns regarding consumer health and transparency. This study introduces an automated approach using IoT and machine learning to detect wax coatings on fruits. Leveraging Convolutional Neural Networks (CNNs), we developed a robust system capable of accurately distinguishing between waxed and unwaxed apples. Our methodology involved collecting a comprehensive dataset, preprocessing images, and training the CNN to classify images based on wax presence. The system demonstrates high accuracy, validated through rigorous testing and comparison with existing methods. This research contributes to enhancing consumer transparency in food safety and sets a foundation for future applications in agricultural technology.