Abstract
There are various qualities of foods which are grown in India. There is an important need to classify these fruits into grades/categories for the farmers to get optimum profits. However, this task is much manual and tedious. Thus, automation can help to get the task done. Here, mango (Mangifera indica L.) classification is performed. In this paper, we picked up seven categories of mangoes, i.e., Aafush, Kesar, Jamadar, Rajapuri, Totapuri, langdo, and Dahseri. First, we have prepared the dataset, and next shape, texture, and pretrained convolutional neural network (CNN) model’s features are extracted and finally classification is performed using linear classifiers. Combination of size parameters and chain code are used for shape, gray level co-occurrence matrix (GLCM), scale-invariant features transform (SIFT) and local binary pattern (LBP) are used for texture identification. Five CNN models namely Inception v3, Xception, ResNet, DenseNet, and MobileNet are used for feature extraction while three linear classifiers support vector machine (SVM), multilayer perceptron neural network (MLP), and K-nearest neighbor (KNN) are used. In experiments, highest Rank-1 accuracy of 98.5% is achieved with features of Inception v3 Model and MLP classifier.
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Naik, S., Rana, Y., Thakkar, V. (2022). Mango (Mangifera Indica L.) Classification Using Shape, Texture and Convolutional Neural Network Features. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Systems and Sustainability. Lecture Notes in Networks and Systems, vol 321. Springer, Singapore. https://doi.org/10.1007/978-981-16-5987-4_25
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DOI: https://doi.org/10.1007/978-981-16-5987-4_25
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