Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning
Fruit recognition becomes more and more important in the agricultural industry. Traditionally, we need to manually identify and label all the fruits in the production line, which is labor intensive, error-prone, and ineffective. Therefore, a lot of fruit recognition systems are created to automate t...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1330 |
| الوسوم: |
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| _version_ | 1857415065175588864 |
|---|---|
| author | Abdo, Ahmed |
| author2 | Hong, Chin Jun Kuan, Lee Meng Pauzi, Maisarah Mohamed Sumari, Putra Abu Zitar, Raed Abualigah, Laith |
| author2_role | author author author author author author |
| author_facet | Abdo, Ahmed Hong, Chin Jun Kuan, Lee Meng Pauzi, Maisarah Mohamed Sumari, Putra Abu Zitar, Raed Abualigah, Laith |
| author_role | author |
| dc.creator.none.fl_str_mv | Abdo, Ahmed Hong, Chin Jun Kuan, Lee Meng Pauzi, Maisarah Mohamed Sumari, Putra Abu Zitar, Raed Abualigah, Laith |
| dc.date.none.fl_str_mv | 2022-11-21T06:01:28Z 2022-11-21T06:01:28Z 2023 |
| dc.identifier.none.fl_str_mv | 9783031175756 1860-949X 1860-9503 https://depot.sorbonne.ae/handle/20.500.12458/1330 10.1007/978-3-031-17576-3_7 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Classification Applications with Deep Learning and Machine Learning Technologies Studies in Computational Intelligence |
| dc.subject.none.fl_str_mv | Markisa Passion fruit Convolutional neural network Deep learning Transfer learning VGG-16 InceptionV3 |
| dc.title.none.fl_str_mv | Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::book::book part |
| description | Fruit recognition becomes more and more important in the agricultural industry. Traditionally, we need to manually identify and label all the fruits in the production line, which is labor intensive, error-prone, and ineffective. Therefore, a lot of fruit recognition systems are created to automate the process, but fruit recognition system for Malaysia local fruit is limited. Thus, this project will focus on classifying one of the Malaysia local fruits which is markisa/passion fruit. We proposed two CNN models for markisa classification. The performances of the proposed models are evaluated on our own dataset collection and produces an accuracy of 97% and 65% respectively. The results indicated that the architecture of CNN model is very important because different architecture can produce different results. Therefore, first CNN model is selected because it can classify 4 types of markisa with a higher accuracy. In the proposed work, we also inspected two transfer learning methods in the classification of markisa which are VGG-16 and InceptionV3. The results showed that the performance of the first proposed CNN model outperforms VGG-16 (95% accuracy) and InceptionV3 (65% accuracy). |
| id | sorbonner_4c6ce30651f15e55aa1d4edd49b9b683 |
| identifier_str_mv | 9783031175756 1860-949X 1860-9503 10.1007/978-3-031-17576-3_7 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1330 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer LearningAbdo, AhmedHong, Chin JunKuan, Lee MengPauzi, Maisarah MohamedSumari, PutraAbu Zitar, RaedAbualigah, LaithMarkisaPassion fruitConvolutional neural networkDeep learningTransfer learningVGG-16InceptionV3Fruit recognition becomes more and more important in the agricultural industry. Traditionally, we need to manually identify and label all the fruits in the production line, which is labor intensive, error-prone, and ineffective. Therefore, a lot of fruit recognition systems are created to automate the process, but fruit recognition system for Malaysia local fruit is limited. Thus, this project will focus on classifying one of the Malaysia local fruits which is markisa/passion fruit. We proposed two CNN models for markisa classification. The performances of the proposed models are evaluated on our own dataset collection and produces an accuracy of 97% and 65% respectively. The results indicated that the architecture of CNN model is very important because different architecture can produce different results. Therefore, first CNN model is selected because it can classify 4 types of markisa with a higher accuracy. In the proposed work, we also inspected two transfer learning methods in the classification of markisa which are VGG-16 and InceptionV3. The results showed that the performance of the first proposed CNN model outperforms VGG-16 (95% accuracy) and InceptionV3 (65% accuracy).2022-11-21T06:01:28Z2022-11-21T06:01:28Z2023Controlled Vocabulary for Resource Type Genres::text::book::book part97830311757561860-949X1860-9503https://depot.sorbonne.ae/handle/20.500.12458/133010.1007/978-3-031-17576-3_7enClassification Applications with Deep Learning and Machine Learning TechnologiesStudies in Computational Intelligenceoai:depot.sorbonne.ae:20.500.12458/13302024-03-10T08:20:08Z |
| spellingShingle | Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning Abdo, Ahmed Markisa Passion fruit Convolutional neural network Deep learning Transfer learning VGG-16 InceptionV3 |
| title | Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning |
| title_full | Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning |
| title_fullStr | Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning |
| title_full_unstemmed | Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning |
| title_short | Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning |
| title_sort | Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning |
| topic | Markisa Passion fruit Convolutional neural network Deep learning Transfer learning VGG-16 InceptionV3 |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1330 |