A Deep Learning-Based Model for Date Fruit Classification

Khalied Albarrak, Yonis Gulzar, Yasir Hamid, Abid Mehmood and Arjumand Bano Soomro
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Khalied Albarrak: Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Yonis Gulzar: Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Yasir Hamid: Information Security and Engineering Technology, Abu Dhabi Polytechnic, Abu Dhabi 111499, United Arab Emirates
Abid Mehmood: Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Arjumand Bano Soomro: Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia

Sustainability, 2022, vol. 14, issue 10, 1-16

Abstract: A total of 8.46 million tons of date fruit are produced annually around the world. The date fruit is considered a high-valued confectionery and fruit crop. The hot arid zones of Southwest Asia, North Africa, and the Middle East are the major producers of date fruit. The production of dates in 1961 was 1.8 million tons, which increased to 2.8 million tons in 1985. In 2001, the production of dates was recorded at 5.4 million tons, whereas recently it has reached 8.46 million tons. A common problem found in the industry is the absence of an autonomous system for the classification of date fruit, resulting in reliance on only the manual expertise, often involving hard work, expense, and bias. Recently, Machine Learning (ML) techniques have been employed in such areas of agriculture and fruit farming and have brought great convenience to human life. An automated system based on ML can carry out the fruit classification and sorting tasks that were previously handled by human experts. In various fields, CNNs (convolutional neural networks) have achieved impressive results in image classification. Considering the success of CNNs and transfer learning in other image classification problems, this research also employs a similar approach and proposes an efficient date classification model. In this research, a dataset of eight different classes of date fruit has been created to train the proposed model. Different preprocessing techniques have been applied in the proposed model, such as image augmentation, decayed learning rate, model checkpointing, and hybrid weight adjustment to increase the accuracy rate. The results show that the proposed model based on MobileNetV2 architecture has achieved 99% accuracy. The proposed model has also been compared with other existing models such as AlexNet, VGG16, InceptionV3, ResNet, and MobileNetV2. The results prove that the proposed model performs better than all other models in terms of accuracy.

Keywords: date fruit classification; artificial intelligence; convolutional neural networks; transfer learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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