• Prajwal Walondre
• Arshan Khan
• Pratham Wankhede
• Manav Gathibandhe
• Sanket Sukhadeve

Automated fruit defect detection is a critical component of quality assurance in the food industry, ensuring consumer satisfaction and product integrity. This study presents a novel approach that integrates image processing techniques with the K-means algorithm to accurately identify defects in fruits. The system comprises a user-friendly webpage interface for seamless interaction and a robust Python-based server to manage the processing pipeline efficiently. Utilizing a diverse dataset encompassing various fruit defects, the model is trained and evaluated meticulously. By leveraging the simplicity and effectiveness of the K-means, the system categorizes defects such as bruises, blemishes, and discolorations based on intricate features extracted from input images.The integration of image processing and machine learning techniques offers a promising avenue for automating quality control processes in the food industry. This research underscores the importance of technological innovation in enhancing productivity and ensuring consistent product quality. Through rigorous experimentation and validation, the proposed system demonstrates its capability to accurately detect and classify fruit defects, thereby mitigating potential risks and minimizing waste. By providing a scalable solution adaptable to different fruit types and defect classes, the system not only streamlines defect detection but also enhances consumer confidence and brand reputation.In conclusion, the fusion of image processing and machine learning algorithms presents a compelling solution for automating fruit defect detection. The system's effectiveness in accurately identifying defects contributes to improving overall quality control processes in the food industry. As technological advancements continue to evolve, further refinements and enhancements to the proposed system can be expected, ultimately leading to more efficient and reliable defect detection methods. This research underscores the transformative potential of automated systems in ensuring product quality and consumer satisfaction across various industries.

Fruit Defect Detection

"Detection of Fruit Defect with Grading System using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.h592-h596, June-2024, Available :http://www.jetir.org/papers/JETIR2406762.pdf

"Detection of Fruit Defect with Grading System using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pph592-h596, June-2024, Available at : http://www.jetir.org/papers/JETIR2406762.pdf