Tomato Fruit Image Dataset for Deep Transfer Learning-based Defect Detection

© 2019 IEEE. Tomato is considered as one of the vegetable crops with highest demand in the Philippines. Job of the farmers does not end after harvesting since the harvested tomatoes needed to be sorted according to its size. Manual sorting is the most widely recognized strategy in sorting but is ver...

Full description

Saved in:
Bibliographic Details
Main Authors: De Luna, Robert G., Dadios, Elmer P., Bandala, Argel A., Vicerra, Ryan Rhay P.
Format: text
Published: Animo Repository 2019
Online Access:https://animorepository.dlsu.edu.ph/faculty_research/811
https://animorepository.dlsu.edu.ph/context/faculty_research/article/1810/type/native/viewcontent
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: De La Salle University
id oai:animorepository.dlsu.edu.ph:faculty_research-1810
record_format eprints
spelling oai:animorepository.dlsu.edu.ph:faculty_research-18102023-01-10T01:59:54Z Tomato Fruit Image Dataset for Deep Transfer Learning-based Defect Detection De Luna, Robert G. Dadios, Elmer P. Bandala, Argel A. Vicerra, Ryan Rhay P. © 2019 IEEE. Tomato is considered as one of the vegetable crops with highest demand in the Philippines. Job of the farmers does not end after harvesting since the harvested tomatoes needed to be sorted according to its size. Manual sorting is the most widely recognized strategy in sorting but is very dependent on human interpretation and thus, very prone to error. This research proposed a solution that provides sorting of tomato fruit by detection of presence of defect. The study presented the generation of image dataset for a deep learning approach detection of defects based from a single tomato fruit image. Models were implemented using OpenCV libraries and Python programming. A total of 1200 tomato images classified as no defect and with defect are gathered using the improvised image capturing box. These images are used for the training, validation, and testing of the three deep learning models namely; VGG16, InceptionV3, and ResNet50. From this, 240 images are used as testing images to assess independently the performance of the trained models using accuracy and F1-score as performance metrics. Experiment results shown that VGG16 has 95.75-95.92-98.75 training-validation-testing accuracy percentage performance, 56.38-59.24-58.33 for the InceptionV3 model, and 90.58-58.46-64.58 for the ResNet50. Comparative analysis revealed that VGG16 is the best deep learning model to be used in the detection of presence of defect in the tomato fruit based from the dataset gathered. 2019-11-01T07:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/811 https://animorepository.dlsu.edu.ph/context/faculty_research/article/1810/type/native/viewcontent Faculty Research Work Animo Repository
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
description © 2019 IEEE. Tomato is considered as one of the vegetable crops with highest demand in the Philippines. Job of the farmers does not end after harvesting since the harvested tomatoes needed to be sorted according to its size. Manual sorting is the most widely recognized strategy in sorting but is very dependent on human interpretation and thus, very prone to error. This research proposed a solution that provides sorting of tomato fruit by detection of presence of defect. The study presented the generation of image dataset for a deep learning approach detection of defects based from a single tomato fruit image. Models were implemented using OpenCV libraries and Python programming. A total of 1200 tomato images classified as no defect and with defect are gathered using the improvised image capturing box. These images are used for the training, validation, and testing of the three deep learning models namely; VGG16, InceptionV3, and ResNet50. From this, 240 images are used as testing images to assess independently the performance of the trained models using accuracy and F1-score as performance metrics. Experiment results shown that VGG16 has 95.75-95.92-98.75 training-validation-testing accuracy percentage performance, 56.38-59.24-58.33 for the InceptionV3 model, and 90.58-58.46-64.58 for the ResNet50. Comparative analysis revealed that VGG16 is the best deep learning model to be used in the detection of presence of defect in the tomato fruit based from the dataset gathered.
format text
author De Luna, Robert G.
Dadios, Elmer P.
Bandala, Argel A.
Vicerra, Ryan Rhay P.
spellingShingle De Luna, Robert G.
Dadios, Elmer P.
Bandala, Argel A.
Vicerra, Ryan Rhay P.
Tomato Fruit Image Dataset for Deep Transfer Learning-based Defect Detection
author_facet De Luna, Robert G.
Dadios, Elmer P.
Bandala, Argel A.
Vicerra, Ryan Rhay P.
author_sort De Luna, Robert G.
title Tomato Fruit Image Dataset for Deep Transfer Learning-based Defect Detection
title_short Tomato Fruit Image Dataset for Deep Transfer Learning-based Defect Detection
title_full Tomato Fruit Image Dataset for Deep Transfer Learning-based Defect Detection
title_fullStr Tomato Fruit Image Dataset for Deep Transfer Learning-based Defect Detection
title_full_unstemmed Tomato Fruit Image Dataset for Deep Transfer Learning-based Defect Detection
title_sort tomato fruit image dataset for deep transfer learning-based defect detection
publisher Animo Repository
publishDate 2019
url https://animorepository.dlsu.edu.ph/faculty_research/811
https://animorepository.dlsu.edu.ph/context/faculty_research/article/1810/type/native/viewcontent
_version_ 1754713733702090752