Color Analysis and Image Processing Applied in Agriculture

Abstract

Color and appearance are perhaps the first attributes that attract us to a fruit or vegetable. Since the appearance of the product generally determines whether a product is accepted or rejected, measuring the color characteristics becomes an important task. To carry out the analysis of this key attribute for agriculture, it is recommended to use an artificial vision system to capture the images of the samples and then to process them by applying colorimetric routines to extract color parameters in an efficient and nondestructive manner, which makes it a suitable tool for a wide range of applications. The purpose of this chapter is to give an overview on recent development of image processing applied to color analysis from horticultural products, more specifically the practical usage of color image analysis in agriculture. As an example, quantitative values of color are extracted from Habanero Chili Peppers using image processing; the images from the samples were obtained using a desktop configuration of machine vision system. The material presented should be useful for students starting on the field, as well as for researchers looking for state-of-the-art studies and practical applications.

Comprehensive assessment of heat pump dryers for drying agricultural products

“…Consumer acceptance of agricultural products is influenced by colour 97,98 . Colorimeters are tools that are used to determine the colour of foodstuffs.…”

“…Consumer acceptance of agricultural products is influenced by colour. 97,98 Colorimeters are tools that are used to determine the colour of foodstuffs. They use three major colour sensors to detect the light reflected from an object: red, green, and blue, which have the same sensitivity as human eye receptors.…”

Procesamiento de Imagenes Digitales para el Análisis de Enfermedades Oculares

“…Para la etapa de extracción de características se hizo uso de la colorimetría, mediante ella obtendremos información colorimétrica de cada píxel; además al considerar la distribución que tiene todos los pixeles que componen una muestra podremos obtener datos sobre el color dentro del área de interés [16] [17] en nuestro caso el área de interés (ROI) es la pupila. Después de obtener los datos colorimétricos sobre las pupilas en las fotografías, tanto en las que se presentaba un tipo de enfermedad ocular como en las que no, se realiza una descomposición en rangos de colores para dichas imágenes digitales.…”