A probabilistic neural network computer vision system for corn kernel damage evaluation

An investigation was conducted to determine whether image processing and machine vision technology could be used for identification of the damage factor in corn kernels. Prominent types of corn kernel damage were found to be germ damage and blue-eye mold damage. A sample set containing 720 kernels with approximately equal numbers of blue-eye mold-damaged, germ-damaged, and sound kernels was obtained and evaluated by human inspectors and the computer vision system. While the computer vision system developed was slightly less consistent in classification than trained human inspectors, it did prove to be a promising step toward inspection automation;Two probabilistic neural network architectures were implemented. The first network, based on a universal smoothing factor algorithm, was used to segment the collected images into blue-eye mold-damaged, germ-damaged, sound germ, shadow in sound germ, hard starch, and soft starch areas. Morphological features from each of the segmented areas were then input to a second probabilistic neural network which used genetic algorithms to optimize a unique smoothing factor for each network input. Output of the second layer network was overall kernel classification of blue-eye mold-damaged, germ-damaged, and sound. Overall accuracy of classification on unseen images was 78%, 94%, and 93% for blue-eye mold-damaged, germ-damaged, and sound categories, respectively. Correct classification for sound and damaged categories on unseen images was 92% and 93%, respectively

Probabilistic Neural Networks for Segmentation of Features in Corn Kernel Images

A method is presented for clustering of pixel color information to segment features within corn kernel images. Features for blue–eye mold, germ damage, sound germ, shadow in sound germ, hard starch, and soft starch were identified by red, green, and blue (RGB) pixel value inputs to a probabilistic neural network. A data grouping method to obtain an exemplar set for adjustment of the Probabilistic Neural Network (PNN) weights and optimization of a universal smoothing factor is described. Of the 14,427 available exemplars (RGB pixel values sampled from previously collected images), 778 were used for adjustment of the network weights, 737 were used for optimization of the PNN smoothing parameter, and 12,912 were reserved for network validation. Based on a universal PNN smoothing factor of 0.05, the network was able to provide an overall pixel classification accuracy of 86% on calibration data and 75% on unseen data. Much of the misclassification was due to overlap of pixel values among classes. When an additional network layer was added to combine similar classes (blue–eye mold and germ damage, sound germ and shadow in sound germ, and hard and soft starch), network results were significantly enhanced so that accuracy on validation data was 94.7%. Image quality was shown to be important to the success of this algorithm as lighting and camera depth of field effects caused artifacts in the segmented images

A probabilistic neural network computer vision system for corn kernel damage evaluation

An investigation was conducted to determine whether image processing and machine vision technology could be used for identification of the damage factor in corn kernels. Prominent types of corn kernel damage were found to be germ damage and blue-eye mold damage. A sample set containing 720 kernels with approximately equal numbers of blue-eye mold-damaged, germ-damaged, and sound kernels was obtained and evaluated by human inspectors and the computer vision system. While the computer vision system developed was slightly less consistent in classification than trained human inspectors, it did prove to be a promising step toward inspection automation;Two probabilistic neural network architectures were implemented. The first network, based on a universal smoothing factor algorithm, was used to segment the collected images into blue-eye mold-damaged, germ-damaged, sound germ, shadow in sound germ, hard starch, and soft starch areas. Morphological features from each of the segmented areas were then input to a second probabilistic neural network which used genetic algorithms to optimize a unique smoothing factor for each network input. Output of the second layer network was overall kernel classification of blue-eye mold-damaged, germ-damaged, and sound. Overall accuracy of classification on unseen images was 78%, 94%, and 93% for blue-eye mold-damaged, germ-damaged, and sound categories, respectively. Correct classification for sound and damaged categories on unseen images was 92% and 93%, respectively.</p

Probabilistic Neural Networks for Segmentation of Features in Corn Kernel Images

A method is presented for clustering of pixel color information to segment features within corn kernel images. Features for blue–eye mold, germ damage, sound germ, shadow in sound germ, hard starch, and soft starch were identified by red, green, and blue (RGB) pixel value inputs to a probabilistic neural network. A data grouping method to obtain an exemplar set for adjustment of the Probabilistic Neural Network (PNN) weights and optimization of a universal smoothing factor is described. Of the 14,427 available exemplars (RGB pixel values sampled from previously collected images), 778 were used for adjustment of the network weights, 737 were used for optimization of the PNN smoothing parameter, and 12,912 were reserved for network validation. Based on a universal PNN smoothing factor of 0.05, the network was able to provide an overall pixel classification accuracy of 86% on calibration data and 75% on unseen data. Much of the misclassification was due to overlap of pixel values among classes. When an additional network layer was added to combine similar classes (blue–eye mold and germ damage, sound germ and shadow in sound germ, and hard and soft starch), network results were significantly enhanced so that accuracy on validation data was 94.7%. Image quality was shown to be important to the success of this algorithm as lighting and camera depth of field effects caused artifacts in the segmented images.This article is from Applied Engineering in Agriculture 17 (2001): 225–234, doi:10.13031/2013.5447. Posted with permission.</p

Implementing a Computer Vision System for Corn Kernel Damage Evaluation

A computer vision system was developed for evaluation of the total damage factor used in corn grading. Major categories of corn damage in the Midwestern U.S. grain market were blue–eye mold damage and germ damage. Seven hundred twenty kernels were obtained from officially sampled Federal Grain Inspection Service (FGIS) corn samples and classified by inspectors on the Board of Appeals and Review. Inspectors classified these kernels into blue–eye mold, germ–damaged, and sound kernels at an 88% agreement rate. A color vision system and lighting chamber were developed to capture replicate images from each sample kernel. Images were segmented via input of red, green, and blue (RGB) values into a neural network trained to recognize color patterns of blue–eye mold, germ damage, sound germ, shadow in sound germ, hard starch, and soft starch. Morphological features (area and number of occurrences) from each of these color group areas were input to a genetic–based probabilistic neural network for computer vision image classification of kernels into blue–eye mold, germ damage, and sound categories. Correct classification by the network on unseen images was 78, 94, and 93%, respectively. Correct classification for sound and damaged categories on unseen images was 92 and 93%, respectively.This article is from Applied Engineering in Agriculture 17 (2001): 235–240. Posted with permission.</p