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


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


EasyPCC: Benchmark Datasets and Tools for High-Throughput Measurement of the Plant Canopy Coverage Ratio under Field Conditions - Download this document for free, or read online. Document in PDF available to download.

1

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

2

CSIRO Agriculture and Food, Queensland Biosciences Precinct, 306 Carmody Rd., St. Lucia, QLD 4067, Australia

3

Institute College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China

4

Institute of Agricultural Machinery, National Agriculture and Food Research Organization, Kannondai 1-31-1, Tsukuba-shi, Ibaraki 305-0856, Japan

5

School of Agriculture and Food Sciences, Building 8117A NRSM, The University of Queensland, Gatton, QLD 4343, Australia





*

Author to whom correspondence should be addressed.



Abstract 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





Author: Wei Guo 1, Bangyou Zheng 2, Tao Duan 2,3, Tokihiro Fukatsu 4, Scott Chapman 2,5 and Seishi Ninomiya 1,*

Source: http://mdpi.com/



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