MNet: A framework to reduce fruit image misclassification

Abstract

Fast and accurate fruit classification is a major problem in the farming business. To achieve the same, the most popular technique used to build a classification model is “Transfer Learning”, in which the weights of pretrained models are used in a new model to solve different but related problems. This technique assures the fast model building with a reduction in generalization error. After testing a popular image classification models namely, DenseNet161, InceptionV3, and MobileNetV2 on created dataset in which a “misclassification” is observed as a major problem which is overlooked by many researchers. This paper proposed a novel framework called “MNet: Merged Net” which not only improves the accuracy, but also addresses the misclassification problem. In this framework, the fruit classification problem is divided into small problems and build a separate model for each. In the final stage, the results of these models are combined. Two models called as FC_InceptionV3 (Fruit Classification InceptionV3) and MFC_InceptionV3 (Merged Fruit Classification InceptionV3) are created based on popular pretrained model InceptionV3. MFC_InceptionV3 is based on proposed framework. In this work, a dataset consisting of 12000 color images of top fruits in India with “Good” and “Bad” quality labels was created and published. The dataset consists of a total of 12 classes. The proposed framework MNet is tested on the most popular deep learning model called InceptionV3. The results of InceptionV3, FC_InceptionV3, and MFC_InceptionV3 are compared. The experimental results shows that the MFC_InceptionV3 model achieved 99.92% accuracy and moderates the image misclassification problem.