Other formats:
BibTeX
LaTeX
RIS
@article{1834769,
author = {Ullah, Sajid and Henke, Michael and Narisetti, N. and Panzarova, K. and Trtilek, M. and Hejátko, Jan and Gladilin, E.},
article_location = {BASEL},
article_number = {22},
doi = {http://dx.doi.org/10.3390/s21227441},
keywords = {high-throughput plant image analysis; spike detection; spike segmentation; deep learning; automated plant phenotyping},
language = {eng},
issn = {1424-8220},
journal = {IEEE Sensors Journal},
title = {Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods},
url = {https://www.mdpi.com/1424-8220/21/22/7441},
volume = {21},
year = {2021}
}
TY - JOUR
ID - 1834769
AU - Ullah, Sajid - Henke, Michael - Narisetti, N. - Panzarova, K. - Trtilek, M. - Hejátko, Jan - Gladilin, E.
PY - 2021
TI - Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods
JF - IEEE Sensors Journal
VL - 21
IS - 22
SP - „7441“
EP - „7441“
PB - IEEE Sensors Council
SN - 14248220
KW - high-throughput plant image analysis
KW - spike detection
KW - spike segmentation
KW - deep learning
KW - automated plant phenotyping
UR - https://www.mdpi.com/1424-8220/21/22/7441
N2 - Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top of the grain plants growing in field conditions. However, accurate and automated analysis of all fully and partially visible spikes in greenhouse images renders a more challenging task, which was rarely addressed in the past. A particular difficulty for image analysis is represented by leaf-covered, occluded but also matured spikes of bushy crop cultivars that can hardly be differentiated from the remaining plant biomass. To address the challenge of automated analysis of arbitrary spike phenotypes in different grain crops and optical setups, here, we performed a comparative investigation of six neural network methods for pattern detection and segmentation in RGB images, including five deep and one shallow neural network. Our experimental results demonstrate that advanced deep learning methods show superior performance, achieving over 90% accuracy by detection and segmentation of spikes in wheat, barley and rye images. However, spike detection in new crop phenotypes can be performed more accurately than segmentation. Furthermore, the detection and segmentation of matured, partially visible and occluded spikes, for which phenotypes substantially deviate from the training set of regular spikes, still represent a challenge to neural network models trained on a limited set of a few hundreds of manually labeled ground truth images. Limitations and further potential improvements of the presented algorithmic frameworks for spike image analysis are discussed. Besides theoretical and experimental investigations, we provide a GUI-based tool (SpikeApp), which shows the application of pre-trained neural networks to fully automate spike detection, segmentation and phenotyping in images of greenhouse-grown plants.
ER -
ULLAH, Sajid, Michael HENKE, N. NARISETTI, K. PANZAROVA, M. TRTILEK, Jan HEJÁTKO and E. GLADILIN. Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods. \textit{IEEE Sensors Journal}. BASEL: IEEE Sensors Council, 2021, vol.~21, No~22, p.~„7441“, 22 pp. ISSN~1424-8220. Available from: https://dx.doi.org/10.3390/s21227441.
|
