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An Expert System for Classification of Potato Tubers using Image Processing and Artificial Neural Networks
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Ebrahim Ebrahimi
Veröffentlicht/Copyright:
27. September 2012
Abstract
In this paper, a novel approach to classify the potato tubers based on their moisture content has been proposed using image processing and multilayer perceptron (MLP) neural network. Some experiments were conducted on 300 independent potato samples during three storing stages. Images of 576×768 pixel sizes from potato samples, with three different moisture contents, were captured using a color CCD camera. After preprocessing and segmentation, 84 features were extracted from the acquired images. Sensitivity analysis was used for feature selection process. Results of feature reduction showed that features in color spaces are more important than those in texture and fast Fourier transform (FFT). A process of trial-and-test procedure was carried out to find the optimum topology, the number of hidden layers and the number of neurons in hidden layers, of MLP network. Results obtained from the final ANN models showed encouraging accuracy (more than 96.22%) to apply the approach to develop an expert system for online potato quality monitoring.
Published Online: 2012-9-27
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Schlagwörter für diesen Artikel
MLP;
machine vision;
marfona;
non-destructive test;
moisture content
Artikel in diesem Heft
- Article
- Thermal Properties of Polyurethane Films Prepared from Mixed Cellulose, Hemicelluloses and Lignin
- Optimization of Microwave Assisted Extraction Conditions for Total Flavonoids in Toona Sinensis Leaves Using Response Surface Methodology
- Nondestructive Method for Analysis of the Soybean Quality
- 5-hydroxymethyl furfural formation and reaction kinetics of different pekmez samples: effect of temperature and storage
- Effect of drying on the color of St. John’s wort (Hypericum perforatum L.) leaves
- Physicochemical and colour properties of organic and conventional kiwifruits as affected by storage periods
- Physical Properties of Whole Rye Seed (Secale cereal)
- Response Surface Evaluation of the Sensory Quality of Turkish Dry Fermented Sausage (sucuk) as Affected by Ripening Period, Nitrite Level and Heat Treatment
- An Expert System for Classification of Potato Tubers using Image Processing and Artificial Neural Networks
- The Digestibility and Thermal Properties of Fermented Flaxseed Protein
- Effect of Air Velocity and Temperature on the Drying Kinetics of Drumstick Leaves (Moringa Oleifera)
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- Acid Diffusion in Solid Foods
- Degradation studies of organic acids in commercially packed fruit juices: A reverse phase high performance liquid chromatographic approach
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- Modeling Desorption Isotherms and Thermodynamic Properties of Fresh Tea Leaves (Camellia sinensis var. assamica)
- Effects Of Spray Drying On Physical Properties, Total Phenolic Content And Antioxidant Activity Of Carob Molasses
- Using Computational Fluid Dynamics (CFD) for Evaluation of Fluid Flow Through a Gate Valve
- Effect of extrusion cooking on the antioxidant activity of extruded half product snacks made of yellow corn and pumpkin flours
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