Seedling length in wheat determined by image processing using mathematical tools

Abstract

-Seed vigour is determined by a series of laboratory tests, many of these laborious and time-consuming. In view of this, the present study aimed to compare the determination of seedling length in wheat using image processing, with the traditional method, as well as the ability of the method to determine seed vigour. The experimental design was completely randomised, with five replications comprising a single-factor experimental scheme. The data were analysed for normality by the Shapiro-Wilk test and variance analysis using ANOVA at 5% probability. The response variables were correlated using Pearson's test, and the methodologies for determining the length of the shoots and roots, by measuring with a graduated rule and by image processing, were compared using the F-test at 5% probability. To compare mean values between cultivars, Tukey's test at 5% probability was used. The length of the shoots and roots of wheat seedlings, determined by image processing using the Matlab ® mathematical tool, is efficient in separating the cultivars into levels of vigour in a similar way to evaluations used routinely for this purpose. RESUMO -O vigor das sementes é determinado por uma série de testes laboratoriais, muitos destes, trabalhosos e morosos. Em vista disso o presente trabalho teve por objetivo comparar a determinação do comprimento de plântula de trigo através do processamento de imagens com o método tradicional, bem como sua capacidade de determinar o vigor de sementes. O delineamento experimental utilizado foi o inteiramente casualizado com cinco repetições, constituindo um experimento em esquema unifatorial. Os dados foram analisados quanto a sua normalidade pelo teste de Shapirowilk e análise de variância pelo teste de ANOVA a 5% de probabilidade. As variáveis respostas foram correlacionadas pelo teste de Pearson, e as metodologias de determinação do comprimento de parte aérea e de raiz através de medição com régua graduada, e por processamento de imagem foram comparadas através do teste F a 5% de probabilidade. Para comparação de média entre as cultivares utilizou-se o teste de Tukey a 5% de probabilidade. O comprimento de parte aérea e de raiz de plântulas de trigo determinados através do processamento de imagens em ferramenta matemática Matlab ® são eficientes para separar as cultivares em níveis de vigor de forma similar às avaliações rotineiramente utilizadas para essa finalidade.

Assessing the physiological quality of common bean seeds using the Vigor-S® system and its relation to the accelerated aging test

“…Seedling image analysis is generally made by spatial processing, where the position and the color of the pixels are analyzed, using extraction algorithms for this purpose (Brunes et al, 2016). The technique stands out through its greater accuracy, standardization, objectivity, and speed in obtaining consistent results (Castan et al, 2018).…”

Rice seeds vigor through image processing of seedlings

“…Getting to the end of the structure the software detects that the other pixel of the extremity, without neighbor finishing the measure of that structure, thus, returning to search another structure in the image. After each reading of one structure the same is removed from the image and the process continues until there is none structure (BRUNES et al, 2016).…”

“…As examples, the determination of shoot and root length of the seedlings, in which 10-20 seedlings must have measured their shoot and root individually with a graduated ruler, according to the methodology described by NAKAGAWA (1999). This test; although, efficient for vigor measurement, requires a considerable amount of time and it is quite arduous because of the difficulties in positioning each of the seedlings during their measurement, and it may have a limited accuracy due to the irregular shape of seedlings (BRUNES et al, 2016).…”

SAPL®: a free software for determining the physiological potential in soybean seeds1

“…been successfully reported for several species, such as pumpkin (Silva et al 2017), peanut (Barbosa et al 2016), corn (Castan et al 2018), wheat (Brunes et al 2016), sunflower (Rocha et al 2015), bean (Gomes Junior et al 2014) and soybean (Marcos Filho et al 2009, Wendt et al 2014, Yagushi et al 2014, among others. The commercial systems developed for this purpose are powerful tools for collecting image data and making inferences about seed vigor.…”