| Autor: |
Knott, Manuel, Odion, Divinefavour, Sontakke, Sameer, Karwa, Anup, Defraeye, Thijs |
| Rok vydání: |
2024 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| Popis: |
Implementing image-based machine learning in agriculture is often limited by scarce data and annotations, making it hard to achieve high-quality model predictions. This study tackles the issue of postharvest quality assessment of bananas in decentralized supply chains. We propose a method to detect and segment surface defects in banana images using panoptic segmentation to quantify defect size and number. Instead of time-consuming pixel-level annotations, we use weak supervision with coarse labels. A dataset of 476 smartphone images of bananas was collected under real-world field conditions and annotated for bruises and scars. Using the Segment Anything Model (SAM), a recently published foundation model for image segmentation, we generated dense annotations from coarse bounding boxes to train a segmentation model, significantly reducing manual effort while achieving a panoptic quality score of 77.6%. This demonstrates SAM's potential for low-effort, accurate segmentation in agricultural settings with limited data. |
| Databáze: |
arXiv |
| Externí odkaz: |
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