Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm

Abstract

This paper intends to present an automated mango grading system under four stages (1) pre-processing, (2) feature extraction, (3) optimal feature selection and (4) classification. Initially, the input image is subjected to the pre-processing phase, where the reading, sizing, noise removal and segmentation process happens. Subsequently, the features are extracted from the pre-processed image. To make the system more effective, from the extracted features, the optimal features are selected using a new hybrid optimization algorithm termed the lion assisted firefly algorithm (LA-FF), which is the combination of LA and FF, respectively. Then, the optimal features are given for the classification process, where the optimized deep convolutional neural network (CNN) is deployed. As a major contribution, the configuration of CNN is fine-tuned via selecting the optimal count of convolutional layers. This obviously enhances the classification accuracy in grading system. For finetuning the convolutional layers in the deep CNN, the LA-FF algorithm is used so that the classifier is optimized. The grading is evaluated on the basis of healthydiseased, ripeunripe and bigmediumvery big cases with respect to type I and type II measures and the performance of the proposed grading model is compared over the other state-of-the-art models. 1 INTRODUCTION Mango (Mangifera indica L.) belongs to the family Anacardiaceae. These are cultivated commercially and extensively in India, tropical Australia, Thailand, Philippines, Hawaii, the lowlands of SouthEast Africa, and in the lowlands of South and Central America. When exporting the mangoes over other countries, the grading [1-4] is essential for quality consideration. Conventionally, the fruit grading is handled by those trained inspectors and this is considered to be labour-intensive, time-consuming, and inefficient. The majority of the countries consider the size feature for mango grading. Still, it remains to be a complex task due to inappropriate grading. Therefore, the automatic grading process [5-7] is very necessary and helpful. On grading the mangoes, the features such as shape, size, firmness, maturity, and visual defects have to be essentially considered. Due to the advancement of technologies, the grading can be effectively made using image processing and computer vision systems [3-8]. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Automated Fruit-ripeness Categorization for Enhanced Shelf Life: A Case Study on Pakistani Chaunsa Mango Using Fusion of Deep Features

“…The suggested grading model's performance is also compared to existing state-of-the-art models. Performance analysis of the proposed classifier using optimal features shows 86.8%, 92.5%, and 89.2% accuracy for HD, RU, and BMV test cases [17].…”

“…The lion-assisted firefly (LA-FF) algorithm is used for the optimization of selected features after attaining the refined features from images. For optimization of CNN, LA-FF is used for fine-tuning of convolutional layers of CNN [17]. The suggested grading model's performance is also compared to existing state-of-the-art models.…”

Importance of Machine Vision Framework with Nondestructive Approach for Fruit Classification and Grading: A Review

“…The study appears to be focused on developing a system for automated mango quality grading using spectroscopic data, and the passage describes the data collection, pre-processing, and analysis steps involved in the research. The author presents a framework based on electrical impedance spectroscopy to identify maturity levels to assess mango quality [13]. In this, 'Tommy Atkins' variety of mango has been utilized in the experiment.…”

Classification and Diseases Identification of Mango Based on Artificial Intelligence: A Review

“…12 species of mango were classified in [35], 2000 samples from 12 different species of mango were captured, pre-processing steps such that: resizing, labelling of species and scaling were performed. Another approach [36] used for mango grading, mango was classified into: healthy, disease, ripe, unripe, big, medium, very big. 748 samples of mangoes were captured from significant database, the samples were divided into: 169 healthy images, 34 diseased, 192 ripe, 164 unripe, 97 belong to big mango, 41 belong to medium mango and 49 belong to very big mango then pre-processing like: image resizing, noise removing, image segmentation and dilation /erosion were performed.…”

“…In [35] CNN with transfer learning used to classify mango, 2000 mango samples were used, 80% of samples for training and the rest samples 20% for testing and validation. In [36] shape, texture and color features were extracted then lion assisted firefly algorithm was used to select relevant features, the selected features fed to CNN, at last mango category was identified. Ten features from three types of features: physical features, electrical features and biochemical features were extracted in [37] then fed to machine learning for classification.…”