A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages
Pomegranates are nutrient-rich fruits renowned for their vibrant ruby-red seeds and antioxidant properties. With a rich history rooted in various cultures, pomegranates have gained widespread popularity for their distinct flavor and potential health benefits. Timely detection and understanding of th...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10433135/ |
| _version_ | 1827132839982792704 |
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| author | Aisha Naseer Madiha Amjad Ali Raza Kashif Munir Nagwan Abdel Samee Manal Abdullah Alohali |
| author_facet | Aisha Naseer Madiha Amjad Ali Raza Kashif Munir Nagwan Abdel Samee Manal Abdullah Alohali |
| author_sort | Aisha Naseer |
| collection | DOAJ |
| description | Pomegranates are nutrient-rich fruits renowned for their vibrant ruby-red seeds and antioxidant properties. With a rich history rooted in various cultures, pomegranates have gained widespread popularity for their distinct flavor and potential health benefits. Timely detection and understanding of the growth stages of pomegranates can facilitate optimized resource allocation, targeted interventions, and efficient crop management. Additionally, early detection contributes to maximizing crop yield, ensuring product quality, and mitigating potential risks such as diseases and pest infestations. The primary goal of the present study is the early detection of pomegranate growth stages using an efficient approach. We conducted our experiments using standard image data of the pomegranate growth stages, comprising 5857 files categorized into five classes: Bud, Early-Fruit, Flower, Mid-growth, and Ripe. We propose a transfer learning-based CRnet approach to capture spatial features from pomegranate images depicting the five stages of pomegranate growth. The extracted spatial features serve as inputs for the random forest method, resulting in the creation of a new probabilistic feature set. These new probabilistic features assist the proposed model in performing the detection of pomegranate growth stages. To evaluate performance, we implemented state-of-the-art image classification techniques, including a Convolutional Neural Network (CNN), K-Neighbors Classifier (KNC), Gaussian Naive Bayes (GNB), and Logistic Regression (LR). To ensure the accuracy of applied machine learning methods, we utilized a hyperparameter optimization approach and a k-fold-based cross-validation technique. Additionally, computational complexity is determined. Extensive analysis of research results shows that by using the proposed features, the random forest model outperformed state-of-the-art methods with a high accuracy of 98% in predicting pomegranate growth stages. Our proposed scheme has the potential for the timely detection of pomegranate growth stages, assisting farmers in maximizing crop yield and mitigating potential risks. |
| first_indexed | 2024-03-07T19:46:52Z |
| format | Article |
| id | doaj.art-e7498e9bd5914534994876f0ceb4ada9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2025-03-20T16:54:22Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e7498e9bd5914534994876f0ceb4ada92024-08-28T23:01:02ZengIEEEIEEE Access2169-35362024-01-0112270732708710.1109/ACCESS.2024.336535610433135A Novel Transfer Learning Approach for Detection of Pomegranates Growth StagesAisha Naseer0https://orcid.org/0009-0007-7013-8821Madiha Amjad1https://orcid.org/0000-0002-1297-8757Ali Raza2https://orcid.org/0000-0001-5429-9835Kashif Munir3https://orcid.org/0000-0001-5114-4213Nagwan Abdel Samee4https://orcid.org/0000-0001-5957-1383Manal Abdullah Alohali5https://orcid.org/0000-0002-1975-5345Institute of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanInstitute of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanInstitute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanInstitute of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi ArabiaPomegranates are nutrient-rich fruits renowned for their vibrant ruby-red seeds and antioxidant properties. With a rich history rooted in various cultures, pomegranates have gained widespread popularity for their distinct flavor and potential health benefits. Timely detection and understanding of the growth stages of pomegranates can facilitate optimized resource allocation, targeted interventions, and efficient crop management. Additionally, early detection contributes to maximizing crop yield, ensuring product quality, and mitigating potential risks such as diseases and pest infestations. The primary goal of the present study is the early detection of pomegranate growth stages using an efficient approach. We conducted our experiments using standard image data of the pomegranate growth stages, comprising 5857 files categorized into five classes: Bud, Early-Fruit, Flower, Mid-growth, and Ripe. We propose a transfer learning-based CRnet approach to capture spatial features from pomegranate images depicting the five stages of pomegranate growth. The extracted spatial features serve as inputs for the random forest method, resulting in the creation of a new probabilistic feature set. These new probabilistic features assist the proposed model in performing the detection of pomegranate growth stages. To evaluate performance, we implemented state-of-the-art image classification techniques, including a Convolutional Neural Network (CNN), K-Neighbors Classifier (KNC), Gaussian Naive Bayes (GNB), and Logistic Regression (LR). To ensure the accuracy of applied machine learning methods, we utilized a hyperparameter optimization approach and a k-fold-based cross-validation technique. Additionally, computational complexity is determined. Extensive analysis of research results shows that by using the proposed features, the random forest model outperformed state-of-the-art methods with a high accuracy of 98% in predicting pomegranate growth stages. Our proposed scheme has the potential for the timely detection of pomegranate growth stages, assisting farmers in maximizing crop yield and mitigating potential risks.https://ieeexplore.ieee.org/document/10433135/Pomegranatespomegranate growthmachine learningimage processingdeep learningtransfer learning |
| spellingShingle | Aisha Naseer Madiha Amjad Ali Raza Kashif Munir Nagwan Abdel Samee Manal Abdullah Alohali A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages IEEE Access Pomegranates pomegranate growth machine learning image processing deep learning transfer learning |
| title | A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages |
| title_full | A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages |
| title_fullStr | A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages |
| title_full_unstemmed | A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages |
| title_short | A Novel Transfer Learning Approach for Detection of Pomegranates Growth Stages |
| title_sort | novel transfer learning approach for detection of pomegranates growth stages |
| topic | Pomegranates pomegranate growth machine learning image processing deep learning transfer learning |
| url | https://ieeexplore.ieee.org/document/10433135/ |
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