Color Classifier for Symptomatic Soybean Seeds Using Image Processing
Open Access
- 1 April 1999
- journal article
- Published by Scientific Societies in Plant Disease
- Vol. 83 (4), 320-327
- https://doi.org/10.1094/pdis.1999.83.4.320
Abstract
Symptoms associated with fungal damage, viral diseases, and immature soybean (Glycine max) seeds were characterized using image processing techniques. A Red, Green, Blue (RGB) color feature-based multivariate decision model discriminated between asymptomatic and symptomatic seeds for inspection and grading. The color analysis showed distinct color differences between the asymptomatic and symptomatic seeds. A model comprising six color features including averages, minimums, and variances for RGB pixel values was developed for describing the seed symptoms. The color analysis showed that color alone did not adequately describe some of the differences among symptoms. Overall classification accuracy of 88% was achieved using a linear discriminant function with unequal priors for asymptomatic and symptomatic seeds with highest probability of occurrence. Individual classification accuracies were asymptomatic 97%, Alternaria spp. 30%, Cercospora spp. 83%, Fusarium spp. 62%, green immature seeds 91%, Phomopsis spp. 45%, soybean mosaic potyvirus (black) 81%, and soybean mosaic potyvirus (brown) 87%. The classifier performance was independent of the year the seed was sampled. The study was successful in developing a color classifier and a knowledge domain based on color for future development of intelligent automated grain grading systems.Keywords
This publication has 10 references indexed in Scilit:
- Evaluation of Colour Representations for Maize ImagesJournal of Agricultural Engineering Research, 1996
- VTFIT: A Microcomputer-based Routine for Fitting Probability Distribution Functions to DataApplied Engineering in Agriculture, 1993
- Phomopsis Seed Decay of Soybeans- A Prototype for Studying Seed DiseasePlant Disease, 1993
- A Trainable Algorithm for Inspection of Soybean Seed QualityTransactions of the ASAE, 1992
- Discoloration of Soybean Seeds- An Indicator of QualityPlant Disease, 1992
- Computer image analyses for detection of maize and soybean kernel quality factorsJournal of Agricultural Engineering Research, 1989
- An Automated Kernel Positioning Device for Computer Vision Analysis of GrainTransactions of the ASAE, 1989
- Physical Properties of Soybean Seeds Damaged by Fungi and a VirusTransactions of the ASAE, 1989
- Evaluating Quality Factors of Corn and Soybeans Using a Computer Vision SystemTransactions of the ASAE, 1988
- Discriminant Functions When Covariance Matrices are UnequalJournal of the American Statistical Association, 1974