Quality assessment of mangoes using convolutional neural network

• Main Authors: Puno, John Carlo V., Billones, Robert Kerwin D., Bandala, Argel A., Dadios, Elmer P., Calilune, Edwin J., Joaquin, Arlene C.
• Format: text
• Published: Animo Repository 2019
• Subjects: Mango—Grading > Philippines; Mango—Philippines—Quality control; Image processing; Neural networks (Computer science); Electrical and Electronics
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/3017
• Institution: De La Salle University

Philippines is one of the countries in the world known for exporting good quality crops. Mangoes in the Philippines are very popular for its good sweet taste and considerably one of the best. Hence, ensuring the quality of the crop to be exported is essential. The study focused on utilizing convolutional neural network in determining the quality of carabao mango (Mangifera Indica). To make sure that all sides of the mango is going to be considered for the quality assessment, a mechanical system that uses conveyor belt, rollers, and camera was used to gather videos for training and validation of the model. The videos were extracted into frames and gone through image processing to remove the background and retain the mango only. The dataset is composed of different mangoes having both good and bad qualities. The implemented model used a total of 5550 training samples with 94.99% accuracy and a total of 2320 samples used for validation with an accuracy of 97.21%. © 2019 IEEE.