Ensemble deep learning approach for apple fruitlet detection from digital images
Agriculture commodities are commodities that have a high economic worth and the potential to be developed further. The green and red apple, in instance, is one type of fruit that has the potential to be cultivated as part of agriculture. In most cases, the sorting of green apples is done manually...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/116336/ http://psasir.upm.edu.my/id/eprint/116336/1/116336.pdf |
| _version_ | 1848866978281816064 |
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| author | Yusof, Mohamad Yusnisyahmi Ishak, Iskandar Sidi, Fatimah |
| author_facet | Yusof, Mohamad Yusnisyahmi Ishak, Iskandar Sidi, Fatimah |
| author_sort | Yusof, Mohamad Yusnisyahmi |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Agriculture commodities are commodities that have a high economic worth and the potential to
be developed further. The green and red apple, in instance, is one type of fruit that has the
potential to be cultivated as part of agriculture. In most cases, the sorting of green apples is
done manually, and individuals are the ones who make the final determinations. The process of
manually identifying products can have several drawbacks, including the fact that it takes a
considerable amount of time, the fact that humans can become fatigued and overworked
when performing repetitive tasks, the fact that there is less variety in the products that can be
identified, and so on. As a result of developments in science as well as digital image
processing technology, it is now possible to automatically categorizing agricultural products and
plantings. The purpose of this research is to enhance the performance of the CNN-based model
in detection of apple fruitlet from apple tree images. A dataset containing 720 images of apple
fruitlet is used in this project. To enhance the overall performance of the model, the revised
CNN-based YOLOv5 ensemble model was implemented with the Sigmoid Linear Unit (SiLU)
activation function, Batch Normalization, and SGD optimization algorithms. The
combination of activation function, optimization, batch normalization, and ensemble
technique are later used to enhance the YOLOv5 ensemble model with the benefits of utilizing
limited resources. According to the experimental results, the accuracy of the updated
ensemble model achieved 95% percent of accuracy in terms of Mean Average Precision (MAP)
when compared to the benchmark model. |
| first_indexed | 2025-11-15T14:29:11Z |
| format | Conference or Workshop Item |
| id | upm-116336 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:29:11Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1163362025-03-25T02:32:48Z http://psasir.upm.edu.my/id/eprint/116336/ Ensemble deep learning approach for apple fruitlet detection from digital images Yusof, Mohamad Yusnisyahmi Ishak, Iskandar Sidi, Fatimah Agriculture commodities are commodities that have a high economic worth and the potential to be developed further. The green and red apple, in instance, is one type of fruit that has the potential to be cultivated as part of agriculture. In most cases, the sorting of green apples is done manually, and individuals are the ones who make the final determinations. The process of manually identifying products can have several drawbacks, including the fact that it takes a considerable amount of time, the fact that humans can become fatigued and overworked when performing repetitive tasks, the fact that there is less variety in the products that can be identified, and so on. As a result of developments in science as well as digital image processing technology, it is now possible to automatically categorizing agricultural products and plantings. The purpose of this research is to enhance the performance of the CNN-based model in detection of apple fruitlet from apple tree images. A dataset containing 720 images of apple fruitlet is used in this project. To enhance the overall performance of the model, the revised CNN-based YOLOv5 ensemble model was implemented with the Sigmoid Linear Unit (SiLU) activation function, Batch Normalization, and SGD optimization algorithms. The combination of activation function, optimization, batch normalization, and ensemble technique are later used to enhance the YOLOv5 ensemble model with the benefits of utilizing limited resources. According to the experimental results, the accuracy of the updated ensemble model achieved 95% percent of accuracy in terms of Mean Average Precision (MAP) when compared to the benchmark model. 2024 Conference or Workshop Item NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/116336/1/116336.pdf Yusof, Mohamad Yusnisyahmi and Ishak, Iskandar and Sidi, Fatimah (2024) Ensemble deep learning approach for apple fruitlet detection from digital images. In: MadeAI Conference, 2-5 July 2024, Portugal. (p. 251). (Submitted) https://madeai-eng.org/2024-conference-program/ |
| spellingShingle | Yusof, Mohamad Yusnisyahmi Ishak, Iskandar Sidi, Fatimah Ensemble deep learning approach for apple fruitlet detection from digital images |
| title | Ensemble deep learning approach for apple fruitlet detection from digital images |
| title_full | Ensemble deep learning approach for apple fruitlet detection from digital images |
| title_fullStr | Ensemble deep learning approach for apple fruitlet detection from digital images |
| title_full_unstemmed | Ensemble deep learning approach for apple fruitlet detection from digital images |
| title_short | Ensemble deep learning approach for apple fruitlet detection from digital images |
| title_sort | ensemble deep learning approach for apple fruitlet detection from digital images |
| url | http://psasir.upm.edu.my/id/eprint/116336/ http://psasir.upm.edu.my/id/eprint/116336/ http://psasir.upm.edu.my/id/eprint/116336/1/116336.pdf |