Classification and Grading of Harvested Mangoes Using Convolutional Neural Network
Hafiz Muhammad Rizwan Iqbal, Ayesha Hakim
Abstract
Mango (Mangifera Indica L. Family Anacardiaceae) is a climatic fruit with a short shelf life. A significant percentage of fruit is wasted each year due to the time-consuming manual grading and classification process. There is a need to replace the traditional methods by adopting automation technologies in the agriculture sector. This paper presents a deep learning-based approach for automated classification and grading of eight cultivars of harvested mangoes based on quality features such as color, size, shape, and texture. Five types of data augmentation methods were used: images rotation, translation, zooming, shearing, and horizontal flip. We compared three architectures of 3-layer Convolutional Neural Network (CNN): VGG16, ResNet152, and Inception v3 on augmented data. The proposed approach achieved up to 99.2% classification accuracy and 96.7% grading accuracy respectively using the Inception v3 architecture of CNN.