Automatic grading of Bi-colored apples by multispectral machine vision

Abstract

In this paper we present a novel application work for grading of apple fruits by machine vision. Following precise segmentation of defects by minimal confusion with stem/calyx areas on multispectral images, statistical, textural and geometric features are extracted from the segmented area. Using these features, statistical and syntactical classifiers are trained for two-and multi-category grading of the fruits. Results showed that feature selection provided improved performance by retaining only the important features, and statistical classifiers outperformed their syntactical counterparts. Compared to the state-of-the-art, our two-category grading solution achieved better recognition rates (93.5% overall accuracy). In this work we further provided a more realistic multi-category grading solution, where different classification architectures are evaluated. Our observations showed that the single-classifier architecture is computationally less demanding, while the cascaded one is more accurate.

Intelligence and its Application in Agriculture: Techniques to Deal with Variations and Uncertainties

“…The major focus and intension of the technique is to use existing data to invent new facts and to uncover new relationships previously unknown even to the experts [21]. In Agriculture, Data Mining techniques have been recently used for recognizing and grading fruits [22]. In China, the relation between climate change, water resources and agriculture was undertaken using the technique of Data Mining [23].…”

A Simple Multispectral Imaging Algorithm for Detection of Defects on Red Delicious Apples

“…Several recent research studies have been carried out to develop image-processing methods for the detection of defective apples (Bhatt et al, 2012;Garrido-Novell et al, 2012;Kim et al, 2007;Srivastava, 2012;Unay et al, 2011). Using processing and analysis methods for full-target images, these studies found that effective algorithms could be developed for non-destructive detection of defective apples using a variety of machine vision systems.…”

Quality evaluation of apple fruit: A Survey

“…Once the color space is specified, color feature can be extracted from images or regions. A number of important color features have been proposed in the literatures, including color histogram [13], color moments(CM) , color coherence vector (CCV) and color correlogram , etc [22]. Texture is one of the important features of an image.…”

“…Jonagold, Fuji). Inspection of the latter group by image processing is more problematic because of color transition areas [13]. According to a plant pathology factsheet, common diseases found in apple are: Apple Scab, Apple Rot, Apple Blotch and Cork Spots.…”