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Counting Maize Kernels through Digital Image Analysis
Author(s) -
Severini Alan D.,
Borrás Lucas,
Cirilo Alfredo G.
Publication year - 2011
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2011.03.0147
Subject(s) - kernel (algebra) , digital image analysis , digital image , grain yield , artificial intelligence , biology , sample (material) , software , digital imaging , zea mays , digital camera , statistics , image processing , computer vision , mathematics , computer science , pattern recognition (psychology) , image (mathematics) , agronomy , chromatography , combinatorics , programming language , chemistry
Because kernel number is the main yield component in grain crops, kernel number per plant or per unit land area is a commonly measured trait in field experiments. This is usually done by means of manual or mechanical counters, which is time consuming. We examined the possibility of counting kernels by means of image analysis. Our method involved taking photographs of dispersed kernel samples with a domestic digital camera and using the open source software ImageJ for automatic particle counting. We show that this is a quick (∼2 min per sample) and reliable (RMSE = 16 kernels per sample) method that could be widely adopted.