Smart automated fruit freshness recognition system using image processing and deep learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
| المؤلفون الرئيسيون: | , , , , |
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| التنسيق: | أطروحة |
| اللغة: | English |
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Brac University
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10361/17929 |
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10361-17929 |
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10361-179292023-03-22T05:18:50Z Smart automated fruit freshness recognition system using image processing and deep learning Shil, Prantha Rahman, Zisanur Bin Jalil, Jawad Bin Sakib, Kazi Rishad Hossain, Md. Tamim Uddin, Dr. Jia Bin Ashraf, Faisal Department of Computer Science and Engineering, Brac University CNN VGG19 Deep Learning Fruit Freshness Regression Image Recognition Keras application. Optical data processing. 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 33-34). Bangladesh is one such country with a tropical monsoon climate typified by significant seasonal rainfall, high temperatures, and high humidity. A wide range of tropical and subtropical fruits are abundant in Bangladesh. The fruits that are most frequently grown are mango, jackfruit, pineapple, banana, litchi, lemon, guava, wood apple, papaya, tamarind, watermelon, pomegranate, plum, etc. Automated fruit recognition is essential since fruits in Bangladesh’s markets come in a variety of types and qualities. This thesis presents a deep learning-based automated fruit recognition model that uses image processing and deep learning architecture to identify fruits and grade their quality. We will make use of our dataset of Bangladeshi fruits for the experimental evaluation. This thesis aims to provide a novel Convolution Neural Network (CNN) structure, called VGG19, for identifying, classifying, and evaluating fruit objects according to their freshness. An application for Keras called VGG19 has a high degree of accuracy in object detection. The outcomes show that our method works better than the linear predictive model and demonstrate its particular merit. Prantha Shil Zisanur Rahman Jawad Bin Jalil Kazi Rishad Bin Sakib Md. Tamim Hossain 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. 2023-03-01T08:44:02Z 2023-03-01T08:44:02Z 2022 2022-09 Thesis ID: 18301219 ID: 18301025 ID: 22141044 ID: 18301274 ID: 19101417 http://hdl.handle.net/10361/17929 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. 34 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
CNN VGG19 Deep Learning Fruit Freshness Regression Image Recognition Keras application. Optical data processing. Artificial intelligence. |
| spellingShingle |
CNN VGG19 Deep Learning Fruit Freshness Regression Image Recognition Keras application. Optical data processing. Artificial intelligence. Shil, Prantha Rahman, Zisanur Bin Jalil, Jawad Bin Sakib, Kazi Rishad Hossain, Md. Tamim Smart automated fruit freshness recognition system using image processing and 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 |
Uddin, Dr. Jia |
| author_facet |
Uddin, Dr. Jia Shil, Prantha Rahman, Zisanur Bin Jalil, Jawad Bin Sakib, Kazi Rishad Hossain, Md. Tamim |
| format |
Thesis |
| author |
Shil, Prantha Rahman, Zisanur Bin Jalil, Jawad Bin Sakib, Kazi Rishad Hossain, Md. Tamim |
| author_sort |
Shil, Prantha |
| title |
Smart automated fruit freshness recognition system using image processing and deep learning |
| title_short |
Smart automated fruit freshness recognition system using image processing and deep learning |
| title_full |
Smart automated fruit freshness recognition system using image processing and deep learning |
| title_fullStr |
Smart automated fruit freshness recognition system using image processing and deep learning |
| title_full_unstemmed |
Smart automated fruit freshness recognition system using image processing and deep learning |
| title_sort |
smart automated fruit freshness recognition system using image processing and deep learning |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/17929 |
| work_keys_str_mv |
AT shilprantha smartautomatedfruitfreshnessrecognitionsystemusingimageprocessinganddeeplearning AT rahmanzisanur smartautomatedfruitfreshnessrecognitionsystemusingimageprocessinganddeeplearning AT binjaliljawad smartautomatedfruitfreshnessrecognitionsystemusingimageprocessinganddeeplearning AT binsakibkazirishad smartautomatedfruitfreshnessrecognitionsystemusingimageprocessinganddeeplearning AT hossainmdtamim smartautomatedfruitfreshnessrecognitionsystemusingimageprocessinganddeeplearning |
| _version_ |
1814308245433483264 |