Global Wheat Head Detection Challenges: Winning Models and Application for Head Counting-Reference-Cited by-同舟云学术

Global Wheat Head Detection Challenges: Winning Models and Application for Head Counting

Published:2023-01 Issue: Volume:5 Page:
ISSN:2643-6515
Container-title:Plant Phenomics
language:en
Short-container-title:Plant Phenomics

Author:

David Etienne¹²,Ogidi Franklin³,Smith Daniel⁴,Chapman Scott⁴,de Solan Benoit²,Guo Wei⁵,Baret Frederic¹,Stavness Ian³

Affiliation:

1. UMR 1114 EMMAH, INRAE, Avignon, France.

2. Arvalis – Institut du Végétal, Paris, France.

3. Department of Computer Science, University of Saskatchewan, Saskatoon, Canada.

4. School of Food and Agricultural Sciences, University of Queensland, Brisbane, Australia.

5. Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.

Abstract

Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems. Data competitions have a rich history in plant phenotyping, and new outdoor field datasets have the potential to embrace solutions across research and commercial applications. We developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions. We analyze the winning challenge solutions in terms of their robustness when applied to new datasets. We found that the design of the competition had an influence on the selection of winning solutions and provide recommendations for future competitions to encourage the selection of more robust solutions.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Agronomy and Crop Science

Link

https://spj.science.org/doi/pdf/10.34133/plantphenomics.0059

Reference46 articles.

1. Harnessing the crowdsourcing power of social media for disaster relief;Gao H;IEEE Intell Syst,2011

2. Crowdsourcing Network Inference: The DREAM Predictive Signaling Network Challenge Meeting Information: The DREAM4 Predictive Signaling Network Challenge took place in the summer of 2009. Results were presented at the DREAM4 conference, December 2009, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.

3. Wiggins A Crowston K. From conservation to crowdsourcing: A typology of citizen Science. Paper presented at IEEE: Proceedings of the 2011 44th Hawaii International Conference on System Sciences ; 2011 January 4–7; Kauai HI.

4. Citizen crowds and experts: Observer variability in image-based plant phenotyping;Giuffrida MV;Plant Methods,2018

5. De novo protein design by citizen scientists

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Sága, a Deep Learning Spectral Analysis Tool for Fungal Detection in Grains—A Case Study to Detect Fusarium in Winter Wheat;Toxins;2024-08-13

2. Application of an image-based head detection method for yield trial plots in wheat and barley breeding programs;Breeding Research;2024-06-01

3. Estimating Fusarium head blight severity in winter wheat using deep learning and a spectral index;The Plant Phenome Journal;2024-05-22

4. Counting manatee aggregations using deep neural networks and Anisotropic Gaussian Kernel;Scientific Reports;2023-11-13