2021/06/10 by Chao Ren, Justin Dulay, Ren, Chao +9
Agricultural and Biological Sciences · Computer Science · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Remote Sensing and LiDAR Applications #Remote Sensing in Agriculture #Smart Agriculture and AI #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2106.05748
CVPR 2021 Agriculture-Vision Workshop
openalex publication_date 2021/06/10 · arxiv created 2021/06/22 · arxiv updated 2021/06/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Automated high throughput plant phenotyping involves leveraging sensors, such as RGB, thermal and hyperspectral cameras (among others), to make large scale and rapid measurements of the physical properties of plants for the purpose of better understanding the difference between crops and facilitating rapid plant breeding programs. One of the most basic phenotyping tasks is to determine the cultivar, or species, in a particular sensor product. This simple phenotype can be used to detect errors in planting and to learn the most differentiating features between cultivars. It is also a challenging visual recognition task, as a large number of highly related crops are grown simultaneously, leading to a classification problem with low inter-class variance. In this paper, we introduce the Sorghum-100 dataset, a large dataset of RGB imagery of sorghum captured by a state-of-the-art gantry system, a multi-resolution network architecture that learns both global and fine-grained features on the crops, and a new global pooling strategy called Dynamic Outlier Pooling which outperforms standard global pooling strategies on this task.