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Sensors 2017, 17(4), 798;

EasyPCC: Benchmark Datasets and Tools for High-Throughput Measurement of the Plant Canopy Coverage Ratio under Field Conditions

International Field Phenomics Laboratory, Institute for Sustainable Agro-ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Midori-cho, Nishitokyo, Tokyo 188-0002, Japan
CSIRO Agriculture & Food, Queensland Biosciences Precinct, 306 Carmody Rd., St. Lucia, QLD 4067, Australia
Institute College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
Institute of Agricultural Machinery, National Agriculture and Food Research Organization, Kannondai 1-31-1, Tsukuba-shi, Ibaraki 305-0856, Japan
School of Agriculture and Food Sciences, Building 8117A NRSM, The University of Queensland, Gatton, QLD 4343, Australia
Author to whom correspondence should be addressed.
Received: 13 February 2017 / Revised: 25 March 2017 / Accepted: 4 April 2017 / Published: 7 April 2017
(This article belongs to the Section State-of-the-Art Sensors Technologies)
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Understanding interactions of genotype, environment, and management under field conditions is vital for selecting new cultivars and farming systems. Image analysis is considered a robust technique in high-throughput phenotyping with non-destructive sampling. However, analysis of digital field-derived images remains challenging because of the variety of light intensities, growth environments, and developmental stages. The plant canopy coverage (PCC) ratio is an important index of crop growth and development. Here, we present a tool, EasyPCC, for effective and accurate evaluation of the ground coverage ratio from a large number of images under variable field conditions. The core algorithm of EasyPCC is based on a pixel-based segmentation method using a decision-tree-based segmentation model (DTSM). EasyPCC was developed under the MATLAB® and R languages; thus, it could be implemented in high-performance computing to handle large numbers of images following just a single model training process. This study used an experimental set of images from a paddy field to demonstrate EasyPCC, and to show the accuracy improvement possible by adjusting key points (e.g., outlier deletion and model retraining). The accuracy (R2 = 0.99) of the calculated coverage ratio was validated against a corresponding benchmark dataset. The EasyPCC source code is released under GPL license with benchmark datasets of several different crop types for algorithm development and for evaluating ground coverage ratios. View Full-Text
Keywords: phenotyping; digital images; plant canopy coverage ratio; field image data phenotyping; digital images; plant canopy coverage ratio; field image data

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Guo, W.; Zheng, B.; Duan, T.; Fukatsu, T.; Chapman, S.; Ninomiya, S. EasyPCC: Benchmark Datasets and Tools for High-Throughput Measurement of the Plant Canopy Coverage Ratio under Field Conditions. Sensors 2017, 17, 798.

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