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Fast and precise fruit classification or recognition as per quality parameter is the unmet need of agriculture business. This is an open research problem, which always attracts researchers. Machine learning and deep learning techniques have shown very promising results for the classification and object detection problems. Neat and clean dataset is the elementary requirement to build accurate and robust machine learning models for the real-time environment. With this objective we have created an image dataset of Indian fruits with quality parameter which are highly consumed or exported. Accordingly, we have considered six fruits namely apple, banana, guava, lime, orange, and pomegranate to create a dataset. The dataset is divided into three folders (1) Good quality fruits (2) Bad quality fruits, and (3) Mixed quality fruits each consists of six fruits subfolders. Total 19,500+ images in the processed format are available in the dataset. We strongly believe that the proposed dataset is very helpful for training, testing and validation of fruit classification or reorganization machine leaning model.

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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.

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