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Classification of Lemon Using Deep Learning

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dc.contributor.author AlZamily, Jawad Yousif
dc.date.accessioned 2020-02-14T15:57:37Z
dc.date.available 2020-02-14T15:57:37Z
dc.date.issued 2020
dc.identifier.uri http://dspace.alazhar.edu.ps/xmlui/handle/123456789/571
dc.description.abstract Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 images belong to 3 species at a few developing phases. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results: found that CNN-driven lemon classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.48% on a held-out test set, demonstrating the feasibility of this approach. en_US
dc.language.iso en_US en_US
dc.subject Deep Learning en_US
dc.subject Classification en_US
dc.subject Lemon en_US
dc.title Classification of Lemon Using Deep Learning en_US
dc.type Article en_US


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