Fruit Freshness Detection Using Android-Based Transfer Learning MobileNetV2

Abstract

Abstract. Fruit is an important part of the source of food nutrition in humans. Fruit freshness is one of the most important factors in selecting fruit that is suitable for consumption. Fruit freshness is also an important factor in determining the price of fruit in the market. So it is very necessary to detect fruit freshness which can be done by machine. Take apples, bananas, and oranges as samples. The machine learning algorithm used in this study uses MobileNetV2 with transfer learning techniques. MobileNetV2 introduces many new ideas aimed at reducing the number of parameters to make it more efficient to run on mobile devices and achieve high classification accuracy. Transfer learning is used so that data does not need training from the start, so it only takes several networks from MobileNetV2 that have previously been trained and then retrained with a different purpose to improve accuracy results. Then the models that have been created are inserted into the application using Android Studio. Software testing is done through black box testing.

Purpose: The purpose of this research is to design a machine-learning model to detect fruit freshness and then apply it to application Android smartphones.

Methods/Study design/approach: The algorithm used in this study uses MobileNetV2 with transfer learning techniques. Models that have been created are inserted into the application using Android Studio.

Result/Findings: The training results using MobileNetV2 transfer learning obtained an accuracy of 99.62% and the loss results obtained were 0.34%. The results of the application after testing using the black box testing method required improvements to the application and the machine learning model so that it can run optimally.

Novelty/Originality/Value: Machine learning models that have been created using transfer learning MobileNetV2 are applied to Android applications so that they can be used by the public.