A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting.

ABSTRACT: The yield of cereal crops such as sorghum (Sorghum bicolor L. Moench) depends on the distribution of crop-heads in varying branching arrangements. Therefore, counting the head number per unit area is critical for plant breeders to correlate with the genotypic variation in a specific breeding field. However, measuring such phenotypic traits manually is an extremely labor-intensive process and suffers from low efficiency and human errors. Moreover, the process is almost infeasible for large-scale breeding plantations or experiments. Machine learning-based approaches like deep convolutional neural network (CNN) based object detectors are promising tools for efficient object detection and counting. However, a significant limitation of such deep learning-based approaches is that they typ

SUBMITTER: Ghosal S 

PROVIDER: S-EPMC7706102 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature