Separation of coriander seeds by Red, Green and Blue image processing

Abstract

Coriander seeds have high socio-economic value in several regions of Brazil, especially in the North and Northeast. Seed maturation determined by color influences the seed quality. With this, digital image processing has become an important tool for separating seeds by color since this classification is usually performed by humans and is highly susceptible to error. The study established parameters for separating coriander seeds by red green and blue (RGB) image analysis, seeking a better selection of coriander seeds according to their color, and evaluating the physiological quality by the germination test. Separation was carried out from two coriander seed lots to obtain samples of 20 g each in three shades: yellowish, gray, and mixed. Images were acquired by the HP C4480 Scanner and processed in the MATLAB software; then, a histogram was constructed for each color analyzed in each sample by the RGB system. ANOVA tested the averages of the scales to ratify the difference in the components’ distributions. The germination test was performed to confirm the results of seed separation using image analysis. The best selection of coriander seeds was achieved by the blue scale, and the germination test indicated that yellow seeds have a higher physiological quality than brownish/greyish seeds.

CLASSIFICATION OF Phaseolus lunatus L. USING IMAGE ANALYSIS AND MACHINE LEARNING MODELS

“…One of the tools to overcome these limitations is the use of image analysis, which enables a fast and economic obtaining of a large quantity of information, require little labor, and is a nondestructive technique (TORRES, 2018). Thus, the use of digital images and a software to assess the results allows a fast evaluation of qualitative and quantitative characters, such as area, length, width, perimeter, and integument color (ABUD et al, 2022) as well as the obtaining of accurate data (MOREIRA et al, 2022).…”