Utilizing EfficientNet for sheep breed identification in low-resolution images
Automatically recognizing sheep breeds is highly valuable for the sheep farming industry, allowing farmers to pinpoint their specific business needs. Accurately distinguishing between sheep breeds poses a challenge for numerous farmers with limited expertise. Although biometric-based identification...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Systems and Soft Computing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S277294192400022X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846115598966194176 |
|---|---|
| author | Galib Muhammad Shahriar Himel Md. Masudul Islam Mijanur Rahaman |
| author_facet | Galib Muhammad Shahriar Himel Md. Masudul Islam Mijanur Rahaman |
| author_sort | Galib Muhammad Shahriar Himel |
| collection | DOAJ |
| description | Automatically recognizing sheep breeds is highly valuable for the sheep farming industry, allowing farmers to pinpoint their specific business needs. Accurately distinguishing between sheep breeds poses a challenge for numerous farmers with limited expertise. Although biometric-based identification offers a feasible solution, its application becomes impractical when assessing large numbers of sheep in real-time. Therefore, the implementation of an automatic sheep classification model that can replicate the breed identification skills of a sheep breed expert can come in handy. This would be particularly beneficial for novice farmers who could utilize handheld devices for breed classification. To address this objective, we propose employing a convolutional neural network (CNN) model capable of rapidly and accurately identifying sheep breeds from low-resolution images. Our experiment utilized a dataset of 1680 facial images representing four distinct sheep breeds. We conducted experiments on the dataset using various EfficientNet models and found that EfficientNetB5 achieved the highest performance with 97.62 % accuracy on a 10 % test split. The classification model we developed has the potential to assist sheep farmers in efficiently distinguishing between different breeds, facilitating more precise assessments and sector-specific classification for various businesses within the industry. |
| format | Article |
| id | doaj-art-665cc9a153b8427bbb32fa16ba2a32f7 |
| institution | Kabale University |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-665cc9a153b8427bbb32fa16ba2a32f72024-12-19T11:03:00ZengElsevierSystems and Soft Computing2772-94192024-12-016200093Utilizing EfficientNet for sheep breed identification in low-resolution imagesGalib Muhammad Shahriar Himel0Md. Masudul Islam1Mijanur Rahaman2Bangladesh University of Business and Technology, Dhaka, BangladeshCorresponding author at: House-36, Road-18, Rupnagar Abashik, Pallabi, Dhaka, 1216, Bangladesh.; Bangladesh University of Business and Technology, Dhaka, BangladeshBangladesh University of Business and Technology, Dhaka, BangladeshAutomatically recognizing sheep breeds is highly valuable for the sheep farming industry, allowing farmers to pinpoint their specific business needs. Accurately distinguishing between sheep breeds poses a challenge for numerous farmers with limited expertise. Although biometric-based identification offers a feasible solution, its application becomes impractical when assessing large numbers of sheep in real-time. Therefore, the implementation of an automatic sheep classification model that can replicate the breed identification skills of a sheep breed expert can come in handy. This would be particularly beneficial for novice farmers who could utilize handheld devices for breed classification. To address this objective, we propose employing a convolutional neural network (CNN) model capable of rapidly and accurately identifying sheep breeds from low-resolution images. Our experiment utilized a dataset of 1680 facial images representing four distinct sheep breeds. We conducted experiments on the dataset using various EfficientNet models and found that EfficientNetB5 achieved the highest performance with 97.62 % accuracy on a 10 % test split. The classification model we developed has the potential to assist sheep farmers in efficiently distinguishing between different breeds, facilitating more precise assessments and sector-specific classification for various businesses within the industry.http://www.sciencedirect.com/science/article/pii/S277294192400022XAgricultural automationComputer visionSheep breed classificationImage processingEfficientNetImage classification |
| spellingShingle | Galib Muhammad Shahriar Himel Md. Masudul Islam Mijanur Rahaman Utilizing EfficientNet for sheep breed identification in low-resolution images Systems and Soft Computing Agricultural automation Computer vision Sheep breed classification Image processing EfficientNet Image classification |
| title | Utilizing EfficientNet for sheep breed identification in low-resolution images |
| title_full | Utilizing EfficientNet for sheep breed identification in low-resolution images |
| title_fullStr | Utilizing EfficientNet for sheep breed identification in low-resolution images |
| title_full_unstemmed | Utilizing EfficientNet for sheep breed identification in low-resolution images |
| title_short | Utilizing EfficientNet for sheep breed identification in low-resolution images |
| title_sort | utilizing efficientnet for sheep breed identification in low resolution images |
| topic | Agricultural automation Computer vision Sheep breed classification Image processing EfficientNet Image classification |
| url | http://www.sciencedirect.com/science/article/pii/S277294192400022X |
| work_keys_str_mv | AT galibmuhammadshahriarhimel utilizingefficientnetforsheepbreedidentificationinlowresolutionimages AT mdmasudulislam utilizingefficientnetforsheepbreedidentificationinlowresolutionimages AT mijanurrahaman utilizingefficientnetforsheepbreedidentificationinlowresolutionimages |