Corn is one of the most important foods and the most widely produced feed grain in the world. It also can be processed into a wide range of industrial products. Production of corn in the United States is 984.37 million metric tons produced in 2014 [1
]. Due to the effects of corn quality in the price and end usage of the corn, grain-grading standards were developed by the U.S. Grain Standards Act of 1916 [2
]. The grain-grading standards are updated and managed by the Federal Grain Inspection Service (FGIS) of the United States Department of Agriculture (USDA). The standards explicitly provided damaged types of corn. Both sellers and buyers now use the standards as a common and worldwide commercial language to decide the type and quality of the corn.
According to the grain-grading standards, corn is classified by moisture, weight, color, shape, odor, and damage [3
]. Among these criteria, the moisture, weight, and odor of corn can be evaluated by special instruments such as an electronic analyzer [4
]. For the criteria about color, shape, and damage, corn grading is generally observed with the naked eye. It is repetitive and tiring, and, consequently, errors can be made. A plausible way to classify corn is using computer vision to classify corn automatically.
Previous studies have demonstrated that computer vision could be a significant way to analyze grains. Zayas et al. used image processing and pattern recognition to identify whole and broken corn kernels [5
]. Ni et al. developed a prototype system to classify whole and broken kernels [6
], corn kernels based on their crown shape [7
], and grade corn based on their size [8
]. Luo et al. used computer vision technology to separate six types of wheat kernels based on their color features [9
]. Steenhoek et al. devised a computer vision system to classify corn based on their damage type [10
]. Dana and Ivo used image processing to categorize flax cultivar based on seed shape and color [11
]. Chen et al combined machine vision and pattern recognition to classify five types of corn based on their shape, color, and geometric features [12
]. Arribas et al. presented an automatic leaf image classification system for sunflower crops using neural networks [13
]. Gao et al. designed a rapid corn sorting algorithm based on machine vision. The proposed corn classification had a speed of 30 ears/min with a 1280 × 1024 pixel CCD camera [14
]. Valiente-Gonzalez et al. devised a computer vision system to automatically evaluate the quality of corn lots by identifying damaged kernels that combined algorithm-based computer vision techniques and principal component analysis (PCA) [15
]. Liu et al. proposed an efficient image processing algorithm to detect parameters such as the length, the number of ear rows, and the quantity of kernels in an ear of corn based on a machine vision [16
]. Mohammad et al. designed an expert system with ant colony optimization (ACO) to automatically recognize different plant species through their leaf images [17
]. Gao et al. designed an automatic detection and classification algorithm for corn product quality and equipment [18
]. The algorithm firstly calculated texture features of fresh corn images through wavelet analysis and then measured the separation degree of texture features by the maximum visual entropy function. Finally, according to the texture features and entropy criterion, the fresh corn products were classified. Sun et al. identified and classified damaged corn kernels including undamaged, insect-damaged, and mildew-damaged by using impact acoustic multi-domain patterns [19
]. Zhang et al. classified three different degrees of freeze-damage in corn seeds using a VIS/NIR hyperspectral imaging system [20
]. Chouhan et al. used computer vision and soft computing methods to identify and classify diseases of the leaf for the plant [21
]. Sajad et al. developed a computer vision algorithm that combined color features and a classifier based on ANN with genetic algorithms for detecting existing fruits in aerial images of an apple cultivar and estimating their ripeness stage [22
]. These proposed systems mainly focused on algorithms, but the research on classification of damaged corn is less and has paid little attention to the design of corn image capture platforms. In addition, most of them worked well for a small scale of corn, however, since touching corn could not be segmented accurately, their performance may be degraded if the number of corns is increased.
This paper proposed a new corn classification method to classify normal and damaged corn. The main contributions of this work are: (a) a set of images of normal corn and six kinds of different damage corns were collected; (b) color and shape features of corn were fused to train a corn classification model; (c) the performances of corn classification conducted on different test sets were evaluated.