Ripe fruit detection and classification using machine learning

• Main Authors: Africa, Aaron Don M., Tabalan, Anna Rovia V., Tan, Mharela Angela A.
• Format: text
• Published: Animo Repository 2020
• Subjects: Machine learning; Fruit—Harvesting—Machinery; Computer vision; Electrical and Computer Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2693
• Institution: De La Salle University

One of the most important sectors in any country is the agricultural sector. However, in some countries, farmers and fishermen have limited technology compared to other developed countries. One of the effects of limited technology is the low quality of crops, fruits, and vegetables. This is because the quality of the products is only assessed depending on external factors like appearance, shape, color, and texture, which can be prone to human error. Determining the quality and ripeness level of fruit requires consistency, which can be hard and tedious for humans when it becomes repetitive work. This paper aims to present various methods and approaches on how ripe fruit detection and classification can be made easier and more convenient using machine learning and machine vision algorithms. Furthermore, this study presents systems that can be utilized in pre and post-harvest analysis. This paper aims to provide solutions using computer applications to help farmers have lesser manual labor yet more accurate data and results in the evaluation of crops. © 2020, World Academy of Research in Science and Engineering. All rights reserved.