Drought Stress Classification using 3D Plant Models

摘要: Quantification of physiological changes in plants can capture different drought mechanisms and assist selection tolerant varieties a high throughput manner. In this context, an accurate 3D model plant canopy provides reliable representation for stress characterization contrast to using 2D images. paper, we propose novel end-to-end pipeline including reconstruction, segmentation feature extraction, leveraging deep neural networks at various stages, study. To overcome the degree self-similarities self-occlusions canopy, prior knowledge leaf shape based on features from siamese network are used construct structure motion wheat plants. The is characterized with aggregation. We compare proposed methodology several descriptors, show that outperforms conventional methods.