Next Article in Journal
Complex Analysis of the Efficiency of Difference Reflectance Indices on the Basis of 400–700 nm Wavelengths for Revealing the Influences of Water Shortage and Heating on Plant Seedlings
Next Article in Special Issue
Novel 3D Imaging Systems for High-Throughput Phenotyping of Plants
Previous Article in Journal
Maximal Instance Algorithm for Fast Mining of Spatial Co-Location Patterns
Previous Article in Special Issue
Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters
Article

Visual Growth Tracking for Automated Leaf Stage Monitoring Based on Image Sequence Analysis

1
Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
2
School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
3
Agricultural Research Division, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Aharon Bar-Hillel
Received: 21 January 2021 / Revised: 19 February 2021 / Accepted: 24 February 2021 / Published: 4 March 2021
In this paper, we define a new problem domain, called visual growth tracking, to track different parts of an object that grow non-uniformly over space and time for application in image-based plant phenotyping. The paper introduces a novel method to reliably detect and track individual leaves of a maize plant based on a graph theoretic approach for automated leaf stage monitoring. The method has four phases: optimal view selection, plant architecture determination, leaf tracking, and generation of a leaf status report. The method accepts an image sequence of a plant as the input and automatically generates a leaf status report containing the phenotypes, which are crucial in the understanding of a plant’s growth, i.e., the emergence timing of each leaf, total number of leaves present at any time, the day on which a particular leaf ceased to grow, and the length and relative growth rate of individual leaves. Based on experimental study, three types of leaf intersections are identified, i.e., tip-contact, tangential-contact, and crossover, which pose challenges to accurate leaf tracking in the late vegetative stage. Thus, we introduce a novel curve tracing approach based on an angular consistency check to address the challenges due to intersecting leaves for improved performance. The proposed method shows high accuracy in detecting leaves and tracking them through the vegetative stages of maize plants based on experimental evaluation on a publicly available benchmark dataset. View Full-Text
Keywords: plant architecture determination; graph theoretic approach; leaf detection; leaf tracking; leaf status report plant architecture determination; graph theoretic approach; leaf detection; leaf tracking; leaf status report
Show Figures

Graphical abstract

MDPI and ACS Style

Bashyam, S.; Choudhury, S.D.; Samal, A.; Awada, T. Visual Growth Tracking for Automated Leaf Stage Monitoring Based on Image Sequence Analysis. Remote Sens. 2021, 13, 961. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050961

AMA Style

Bashyam S, Choudhury SD, Samal A, Awada T. Visual Growth Tracking for Automated Leaf Stage Monitoring Based on Image Sequence Analysis. Remote Sensing. 2021; 13(5):961. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050961

Chicago/Turabian Style

Bashyam, Srinidhi, Sruti D. Choudhury, Ashok Samal, and Tala Awada. 2021. "Visual Growth Tracking for Automated Leaf Stage Monitoring Based on Image Sequence Analysis" Remote Sensing 13, no. 5: 961. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050961

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop