A Study of the characteristic of the observation angle on the terrestrial image measurement of paddy vegetation cover

Takahashi, Kazuhiko; Rikimaru, Atsushi; Sakata, Kenta; Endou, Shigenori

doi:10.4287/jsprs.50.367

Cited by 4 publications

(1 citation statement)

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“…The plant canopy coverage ratio (PCCr) is a parameter often used to indicate plant growth status. It has been reported that the PCCr is strongly correlated with the leaf area index, canopy light interception, nitrogen content, and crop yield [ 7 , 8 , 9 , 10 ]. The PCCr can be estimated easily from images because it is defined as the percentage of the orthogonal projection area relative to the area of crop foliage in the horizontal plane.…”

Section: Introductionmentioning

confidence: 99%

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

Guo

Zheng

Duan

et al. 2017

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%