Detection and Sizing of Durian using Zero-Shot Deep Learning Models
Since 2017, up to 41% of Malaysia's land has been cultivated for durian, making it the most widely planted crop. The rapid increase in demand urges the authorities to search for a more systematic way to control durian cultivation and manage the productivity and quality of the fruit. This re...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2023-10-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/6640 |
| Summary: | Since 2017, up to 41% of Malaysia's land has been
cultivated for durian, making it the most widely planted crop. The rapid increase
in demand urges the authorities to search for a more systematic way to control
durian cultivation and manage the productivity and quality of the fruit. This
research paper proposes a deep-learning approach for detecting and sizing
durian fruit in any given image. The aim is to develop zero-shot learning
models that can accurately identify and measure the size of durian fruits in
images, regardless of the image’s background. The proposed methodology
leverages two cutting-edge models: Grounding DINO and Segment Anything (SAM),
which are trained using a limited number of samples to learn the essential
features of the fruit. The dataset used for training and testing the model
includes various images of durian fruits captured from different sources. The
effectiveness of the proposed model is evaluated by comparing it with the
Segmentation Generative Pre-trained Transformers (SegGPT) model. The results
show that the Grounding DINO model, which has a 92.5% detection accuracy,
outperforms the SegGPT in terms of accuracy and efficiency. This research has
significant implications for computer vision and agriculture, as it can
facilitate automated detection and sizing of durian fruits, leading to improved
yield estimation, quality control, and overall productivity. |
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| ISSN: | 2086-9614 2087-2100 |