Aerial Images and Convolutional Neural Network for Cotton Bloom Detection.

ABSTRACT: Monitoring flower development can provide useful information for production management, estimating yield and selecting specific genotypes of crops. The main goal of this study was to develop a methodology to detect and count cotton flowers, or blooms, using color images acquired by an unmanned aerial system. The aerial images were collected from two test fields in 4 days. A convolutional neural network (CNN) was designed and trained to detect cotton blooms in raw images, and their 3D locations were calculated using the dense point cloud constructed from the aerial images with the structure from motion method. The quality of the dense point cloud was analyzed and plots with poor quality were excluded from data analysis. A constrained clustering algorithm was developed to register the same b

SUBMITTER: Xu R 

PROVIDER: S-EPMC5820543 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature