干旱区生态环境知识资源中心(Arid): A Deep Learning-Based Model for Date Fruit Classification

Arid

DOI	10.3390/su14106339
	A Deep Learning-Based Model for Date Fruit Classification
	Albarrak, Khalied; Gulzar, Yonis; Hamid, Yasir; Mehmood, Abid; Soomro, Arjumand Bano
通讯作者	Albarrak, K ; Gulzar, Y
来源期刊	SUSTAINABILITY
EISSN	2071-1050
出版年	2022
卷号	14 期号:10
英文摘要	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.
英文关键词	date fruit classification artificial intelligence convolutional neural networks transfer learning
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000802670800001
WOS关键词	AGRICULTURE
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394583
推荐引用方式 GB/T 7714	Albarrak, Khalied,Gulzar, Yonis,Hamid, Yasir,et al. A Deep Learning-Based Model for Date Fruit Classification[J],2022,14(10).
APA	Albarrak, Khalied,Gulzar, Yonis,Hamid, Yasir,Mehmood, Abid,&Soomro, Arjumand Bano.(2022).A Deep Learning-Based Model for Date Fruit Classification.SUSTAINABILITY,14(10).
MLA	Albarrak, Khalied,et al."A Deep Learning-Based Model for Date Fruit Classification".SUSTAINABILITY 14.10(2022).