Article: Image-based deep learning automated grading of date fruit (Alhasa case study Saudi Arabia) Journal: International Journal of Computational Vision and Robotics (IJCVR) 2024 Vol.14 No.2 pp.213 - 231 Abstract: Dates are small and popular in the Middle East, and they grow in many countries. Many researchers focus on classifying dates by type. But the researchers did not consider that many date industries sort dates by quality to determine the proper price and use. This paper classifies Tamer stage dates automatically based on quality. This study proposed two ways to differentiate date fruit quality. First, using CNN, VGG-16 is used to extract features from the dataset, and then SVM classifier is used. The second method is based on developed CNN. Tamar used three different images to train these models. Another contribution is the creation of our own dataset, which was acquired using a smartphone camera under uncontrolled lighting and camera parameter circumstances, such as autofocus and camera stabilisation. A comparison between two methods shows that the CNN model had 97% classification accuracy for Khalas, 95% for Ruzaiz and 90% for Shaishi. Inderscience Publishers - linking academia, business and industry through research

Title: Image-based deep learning automated grading of date fruit (Alhasa case study Saudi Arabia)

Authors: Amnah Aldandan; Sajeda AlGhanim; Hawraa Alhashim; Mona A.S. Ali

Addresses: College of Computer Science and Information Technology, King Faisal University, Kingdom of Saudi Arabia ' College of Computer Science and Information Technology, King Faisal University, Kingdom of Saudi Arabia ' College of Computer Science and Information Technology, King Faisal University, Kingdom of Saudi Arabia ' College of Computer Science and Information Technology, King Faisal University, Kingdom of Saudi Arabia; Faculty of Computers and Artificial Intelligence, Benha University, Egypt

Abstract: Dates are small and popular in the Middle East, and they grow in many countries. Many researchers focus on classifying dates by type. But the researchers did not consider that many date industries sort dates by quality to determine the proper price and use. This paper classifies Tamer stage dates automatically based on quality. This study proposed two ways to differentiate date fruit quality. First, using CNN, VGG-16 is used to extract features from the dataset, and then SVM classifier is used. The second method is based on developed CNN. Tamar used three different images to train these models. Another contribution is the creation of our own dataset, which was acquired using a smartphone camera under uncontrolled lighting and camera parameter circumstances, such as autofocus and camera stabilisation. A comparison between two methods shows that the CNN model had 97% classification accuracy for Khalas, 95% for Ruzaiz and 90% for Shaishi.

Keywords: date fruit; classification; Rutab stages; deep learning; convolutional neural network; CNN; support vector machine; SVM; Saudi Arabia.

DOI: 10.1504/IJCVR.2024.136999

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.2, pp.213 - 231

Received: 18 Jan 2022
Accepted: 31 Mar 2022
Published online: 01 Mar 2024 *

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Title: Image-based deep learning automated grading of date fruit (Alhasa case study Saudi Arabia)

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