Fruit and vegetable freshness detection using deep learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
| Main Authors: | , , , , |
|---|---|
| Drugi avtorji: | |
| Format: | Thesis |
| Jezik: | English |
| Izdano: |
Brac University
2023
|
| Teme: | |
| Online dostop: | http://hdl.handle.net/10361/17930 |
| id |
10361-17930 |
|---|---|
| record_format |
dspace |
| spelling |
10361-179302023-03-22T05:19:30Z Fruit and vegetable freshness detection using deep learning Haque, Md. Mahidul Ahmed, Rehanul Saha, Somak Saha, Chamak Dutta, Mayurakshmi Rasel, Annajiat Alim Karim, Dewan Ziaul Mostakim, Moin Department of Computer Science and Engineering, Brac University Freshness Neural Network VGG19 Image Recognition Mo Bilenetv2 Automation K-Fold Image Classification Machine learning. Artificial intelligence. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 76-78). Bangladesh’s economy depends on agriculture, which contributes significantly to GDP. It’s time to automate agriculture for increased productivity, efficiency, and sustainability. Computer Vision can assist in ensuring agricultural product quality. CNN is more efficient than other (ML) algorithms for Computer Vision applications since it automatically extracts features and handles complex problems. We deployed CNN architectures to identify fruit and vegetable freshness. Using Computer Vision technology, we want to make food production, sorting, packaging, and delivery more efficient, inexpensive, feasible, and safe at the production and consumer level. Man ual quality testing is laborious, inaccurate, and time-consuming. In the study, we have compared 7 pre-trained CNN models (VGG19, InceptionV3, EfficientNetV2L, Xception, ResNet152V2, MobileNetV2, and DenseNet201) with our custom, CNN based image classification model, “FreshDNN”. Our custom small Deep Learning model classifies fresh and rotten fruits and vegetables. Using this custom model, users may snap food images to determine their freshness. Farmers may utilize it to embedded systems and map out their agricultural areas on the basis of freshness of their fruits or vegetables. We trained the models on our dataset to recognize fresh and rotting fruit using image data from 8 distinct fruits and vegetables. We observed that FreshDNN had a 99.32% training accuracy, 97.8% validation accu racy and beat pre-trained models in various performance measures like Precision (98%), Recall (98%), F1 Score (98%) except for VGG19. However, our own custom model surpassed every pre-trained model for our dataset in terms of the number of parameters (394,448), training time (65.77 minutes), ROC-AUC score (99.98%), computational cost, and space (4.6 MB). We have also implemented 5-fold cross validation where our model has performed similarly better where train, validation and test accuracy was 99.35%, 97.62% and 97.658% respectively. We believe it will perform comparably better than other pre-trained models. Md. Mahidul Haque Rehanul Ahmed Somak Saha Chamak Saha Mayurakshmi Dutta B. Computer Science 2023-03-01T09:07:42Z 2023-03-01T09:07:42Z 2022 2022-09 Thesis ID: 19101387 ID: 19101548 ID: 19101286 ID: 19101401 ID: 19101410 http://hdl.handle.net/10361/17930 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 78 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Freshness Neural Network VGG19 Image Recognition Mo Bilenetv2 Automation K-Fold Image Classification Machine learning. Artificial intelligence. |
| spellingShingle |
Freshness Neural Network VGG19 Image Recognition Mo Bilenetv2 Automation K-Fold Image Classification Machine learning. Artificial intelligence. Haque, Md. Mahidul Ahmed, Rehanul Saha, Somak Saha, Chamak Dutta, Mayurakshmi Fruit and vegetable freshness detection using deep learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Rasel, Annajiat Alim |
| author_facet |
Rasel, Annajiat Alim Haque, Md. Mahidul Ahmed, Rehanul Saha, Somak Saha, Chamak Dutta, Mayurakshmi |
| format |
Thesis |
| author |
Haque, Md. Mahidul Ahmed, Rehanul Saha, Somak Saha, Chamak Dutta, Mayurakshmi |
| author_sort |
Haque, Md. Mahidul |
| title |
Fruit and vegetable freshness detection using deep learning |
| title_short |
Fruit and vegetable freshness detection using deep learning |
| title_full |
Fruit and vegetable freshness detection using deep learning |
| title_fullStr |
Fruit and vegetable freshness detection using deep learning |
| title_full_unstemmed |
Fruit and vegetable freshness detection using deep learning |
| title_sort |
fruit and vegetable freshness detection using deep learning |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/17930 |
| work_keys_str_mv |
AT haquemdmahidul fruitandvegetablefreshnessdetectionusingdeeplearning AT ahmedrehanul fruitandvegetablefreshnessdetectionusingdeeplearning AT sahasomak fruitandvegetablefreshnessdetectionusingdeeplearning AT sahachamak fruitandvegetablefreshnessdetectionusingdeeplearning AT duttamayurakshmi fruitandvegetablefreshnessdetectionusingdeeplearning |
| _version_ |
1814306880914194432 |