Image Processing Mobile Application for Fruit Maturity Classification

Abstract

This study presents the development of mobile application that classify the maturity of pineapple fruit based on visual interpretation of its color texture using Image Processing. The mobile application classifies pineapple into Unripe, Ripe and Fully Ripe via image processing and machine learning using Tensor Flow. The researcher employed a developmental-descriptive approach in the study using the Fourth Generation Technique as the development paradigm. The accuracy of the mobile application was tested using different camera photography angle. The acceptability of the mobile application was evaluated by one hundred (100) farmers and twenty (20) IT practitioners. The developed device is rated using ISO25010 quality standard which contains the following criteria: Functional Suitability, Performance Efficiency, Usability, Reliability, and Portability. The result of the evaluation showed that the device met all the criteria mentioned.Based on the result of the study, the researchers find out that the image processing was effective and reliable in determining maturity indices. Based on the related studies, all of them proves that computer vision and machine learning can determine diseases, health issues and object classification. Image processing was accurate in proving object classification and it was implemented widely in different field of Information Technology. Image Processing was the future innovation to the industry of information technology in processing data, providing informative results and analyzing different scenarios. Computer Vision can apply in different several of fields like agriculture. The algorithm can help the pre-harvesting and post-harvesting of the different crops.