Fine-Grained Recognition in High-throughput Phenotyping.

Fine-Grained Recognition aims to classify sub-category objects such as bird species and car models from imagery. In High-throughput Phenotyping, the required task is to classify individual plant cultivars to assist plant breeding, which has posed three challenges: 1) it is easy to overfit complex features and models, 2) visual conditions change during and between image collection opportunities, and 3) analysis of thousands of cultivars require high-throughput data collection and analysis. To tackle these challenges, we propose a simple but intuitive descriptor, Radial Object Descriptor, to represent plant cultivar objects based on contour. This descriptor is invariant under scaling, rotation, and translation, as well as robust under changes to the plant's growth stage and camera's view angle. Furthermore, we complement this mid-level feature by fusing it with the low-level features (Histogram of Oriented Gradients) and deep features (ResNet-18), respectively. We extensively test our fusion approaches using two real world experiments. One experiment is on a novel benchmark dataset (HTP-Soy) in which we collect ~ 2, 000 high-resolution aerial images of outdoor soybean plots. Another experiment is on three datasets of indoor rosette plants. For both experiments, our fusion approaches achieve superior accuracies while maintaining better generalization as compared with traditional approaches.