Researchers have developed Eff-3DPSeg, a weakly supervised deep learning framework for 3D plant shoot segmentation. This innovative approach uses a low-cost photogrammetry system and a Meshlab-based Plant Annotator to acquire and annotate point clouds from individual plants. The framework overcomes the challenges of expensive and time-consuming labeling processes and shows promising potential for enhancing high throughput.
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Eff-3DPSeg: A Deep Learning Framework for 3D Organ-Level Plant Shoot Segmentation
Deep learning is now widely used in various fields, including plant-related applications. The Eff-3DPSeg framework represents a significant advance in 3D plant shoot segmentation, offering practical solutions and value for middle managers looking to incorporate AI into their operations.
Practical Solutions and Value:
- Efficient annotation process and accurate segmentation capabilities
- Overcoming challenges of expensive and time-consuming labeling processes through weakly supervised deep learning and innovative annotation techniques
- Potential for enhancing high throughput in plant phenotyping
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- AI Lab in Telegram @aiscrumbot – free consultation
- Meet Eff-3DPSeg: A Deep Learning Framework for 3D Organ-Level Plant Shoot Segmentation
- MarkTechPost
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