| A Review on Machine Learning in Classification of Date Varieties |
| Paper ID : 1259-NICAME1402 |
| Authors: |
|
pouriya rezaie1, Mahdi Keramat-Jahromi2, Mohammad Amin Nematollahi *3, Seyed Mehdi Nassiri4, Sajad Heidari2 1Biosystems Engineering Department, Faculty of Agriculture, Shiraz University, Shiraz, Iran 2Mechanical Engineering of Biosystems Department, Faculty of Agriculture, Shiraz University, Shiraz, Iran 3Biosystems Engineering Department, College of Agriculture, Shiraz University, Shiraz, Iran 4Department of Biosystems Engineering Shiraz University |
| Abstract: |
| Dates are one of the oldest and most valuable agricultural products known in the world. Despite the long history of the production of this product, most of the activities related to harvesting and post-harvest, including grading and packing of dates, are done in the traditional way. Classification and grading in traditional methods by human labor is associated with limitations such as physical damage to the product, error, and fatigue of human labor, it is time-consuming and associated with high costs. Therefore, the development of automatic systems for date classification and grading is of particular importance. In addition to increasing economic efficiency, these systems have increased the marketability of the product by having high speed, high accuracy, and better observance of product hygiene by reducing manual intervention, and as a result, they can increase exports. Each variety of different date varieties has its own characteristics that can be valuable in identifying and determining each variety. Some of these features include shape, color, texture, size, and mass. In this article, a comprehensive review of date fruit classification and grading studies and methods using different machine learning algorithms has been done. |
| Keywords: |
| Machine learning, Classification, Grading, Date fruit, Image processing, Postharvest |
| Status : Paper Accepted (Poster Presentation) |
