Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{181453,
        author = {Veena V, and Nimmi Abdul Nazir and Kasinath S V and Karthik L and Jinu Raj R},
        title = {A Novel Approach for Detecting Diseased Apple in Real Time},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3984-3989},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181453},
        abstract = {This paper presents a comprehensive, real-time apple disease detection and sorting system that leverages modern artificial intelligence and embedded automation technologies. Utilizing a YOLOv5 deep learning model for object detection, Python for image processing, and an Arduino Nano for servo motor control, the system accurately classifies apples into fresh or rotten categories. A robotic arm powered by MG995R servo motors performs the actual sorting operation, ensuring precise and damage-free handling. The entire process is synchronized through efficient serial communication between Python and the microcontroller. This integrated solution not only improves produce quality assurance but also significantly reduces labor dependency, processing time, and operational errors in agricultural and food packaging sectors.},
        keywords = {YOLOv5, Apple Sorting, Real-Time Detection, Computer Vision, Embedded Systems, Automation},
        month = {June},
        }