Jennifer Hobbs
Prajwal Prakash1,2†
Robert Paull
Harutyun Hovhannisyan
Bernard Markowicz
Greg Rose
94% of researchers rate our articles as excellent or good
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Find out more
- 1IntelinAir, Inc., Champaign, IL, United States
- 2Department of Electrical Engineering, Columbia University, New York, NY, United States
- 3Department of Tropical Plant and Soil Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
A Corrigendum on
Large-Scale Counting and Localization of Pineapple Inflorescence Through Deep Density-Estimation
by Hobbs, J., Prakash, P., Paull, R., Hovhannisyan, H., Markowicz, B., and Rose, G. (2021) Front. Plant Sci. 11:599705. doi: 10.3389/fpls.2020.599705
In the original article, there was a mistake in Figure 8 as published. The labels for the Validation and Test losses were switched in the legend. The Validation loss should be indicated as a red line with circular markers and the test loss should be indicated as a red line with starred markers. The text and caption described the figure correctly and therefore remain unchanged. The corrected Figure 8 appears below.
FIGURE 8
Figure 8. Increasing the amount of (labeled) training data in a smart fashion decreases test loss as well as the MAE on the test set. The validation loss slightly increases as more data is added, suggesting less over-fitting is occurring as more data is added.
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Keywords: deep learning-artificial neural network (DL-ANN), active learning, pineapple, computer vision, remote sensing-GIS, weakly supervised, counting, density estimation
Citation: Hobbs J, Prakash P, Paull R, Hovhannisyan H, Markowicz B and Rose G (2021) Corrigendum: Large-Scale Counting and Localization of Pineapple Inflorescence Through Deep Density-Estimation. Front. Plant Sci. 12:695397. doi: 10.3389/fpls.2021.695397
Received: 14 April 2021; Accepted: 15 April 2021;
Published: 17 May 2021.
Approved by:
Frontiers Editorial Office, Frontiers Media SA, SwitzerlandCopyright © 2021 Hobbs, Prakash, Paull, Hovhannisyan, Markowicz and Rose. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jennifer Hobbs, amVubmlmZXImI3gwMDAyQjtyZXNlYXJjaEBpbnRlbGluYWlyLmNvbQ==
†These authors have contributed equally to this work