CLASSIFICATION OF DATES QUALITY USING DEEP LEARNING TECHNOLOGY BASED ON CAPTURED IMAGES | ||||
| Misr Journal of Agricultural Engineering | ||||
| Articles in Press, Accepted Manuscript, Available Online from 18 May 2024 PDF (1.02 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjae.2024.286079.1137 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Waleed Kamel ElHelew 1; Dalia Abo-Bbakr2; Sahar Zayan2; Muhammad Ahmad Mayhoub  3
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| 1Assoc. Prof., Ag. Eng. Dept., Fac. of Ag., Ain Shams U., Al Qalyubiyah, Egypt. | ||||
| 2Head of Res. at the Plant Pathology Research Institute., Ag. Res. Center (A. R. C)., Giza, Egypt. | ||||
| 3Assist. Prof., Ag. Eng. Dept., Fac. of Ag., Ain Shams U., Al Qalyubiyah, Egypt. | ||||
| Abstract | ||||
| Dates are a common fruit in many Middle Eastern and African nations and have religious and cultural value. one of the key elements in judging the quality of dates is sorting according to their health state. Combining rejected dates with accepted ones causes significant economic losses in both storage and exportation. Despite being a crucial stage for obtaining high-quality dates and reducing losses, this sorting process is still conducted using traditional methods. Thus, this study aims to classify date fruit quality (accepted or rejected) with machine learning technology to reduce cost, time, and improve the quality of final product. In this study, several Convolutional Neural Network architectures (Inception-v3, Inception-ResNet-v2, VGG19) were used to classify three varieties of date fruit (Mejdool, Saiedi, El-Wadi). These varieties were classified into accepted and rejected samples to build the dataset im-ages. An Arduino Automatic mobile camera shutter controller captured the dataset images. In addition to the Kaggle dataset which was added to the accepted images. The total dataset consisted of 5,945 images, comprising 3,142 accepted images and 2,803 rejected images. By comparing the results of different architectures, Inception-ResNet-v2 demonstrated the best performance, achieving an accuracy of 98.99% and a loss of 0.0344. Therefore, it can be concluded that the Inception-ResNet-v2 model could be utilized to develop a suitable computer vision system, thereby enhancing the date sorting process and facilitating the packaging of high-quality dates. | ||||
| Keywords | ||||
| AI Techniques; Convolutional Neural Network (CNN); Transfer Learning; Date Fruits Handling; Rejected Fruits | ||||
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