TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks.

ABSTRACT:

Background

Grain yield of wheat is greatly associated with the population of wheat spikes, i.e., spikenumberm-2 . To obtain this index in a reliable and efficient way, it is necessary to count wheat spikes accurately and automatically. Currently computer vision technologies have shown great potential to automate this task effectively in a low-end manner. In particular, counting wheat spikes is a typical visual counting problem, which is substantially studied under the name of object counting in Computer Vision. TasselNet, which represents one of the state-of-the-art counting approaches, is a convolutional neural network-based local regression model, and currently benchmarks the best record on counting maize tassels. However, when applying TasselNet to wheat spikes, it cannot predic

SUBMITTER: Xiong H 

PROVIDER: S-EPMC6905110 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature