TasselNetV2+: A Fast Implementation for High-Throughput Plant Counting From High-Resolution RGB Imagery
Plant counting runs through almost every stage of agricultural production from seed breeding, germination, cultivation, fertilization, pollination to yield estimation, and harvesting. With the prevalence of digital cameras, graphics processing units and deep learning-based computer vision technology...
Main Authors: | Hao Lu, Zhiguo Cao |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2020-12-01
|
Series: | Frontiers in Plant Science |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2020.541960/full |
Similar Items
-
TasselNet: counting maize tassels in the wild via local counts regression network
by: Hao Lu, et al.
Published: (2017-11-01) -
Advancing Tassel Detection and Counting: Annotation and Algorithms
by: Azam Karami, et al.
Published: (2021-07-01) -
Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN
by: Yunling Liu, et al.
Published: (2020-01-01) -
TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks
by: Haipeng Xiong, et al.
Published: (2019-12-01) -
Maize tassels detection: a benchmark of the state of the art
by: Hongwei Zou, et al.
Published: (2020-08-01)