Abstract:
Maize is an economically important crop, which is contributing the highest of all food, industrial product and animal feeding revenues in Ethiopia. It is also the major cash crop of our country and farmers of the maize producing regions. Therefore, maize has a unique quality that fetches premium prices in the world market. However, to exploit this good opportunity the distinct unique shape, color and size of maize need to be maintained and improved. The first step to improve the crop is to understand the exiting variability in the genotype/accession. Therefore, in this research work, a digital image processing technique was used to classify maize seed based on their Color, morphological, and textural features. Artificial neural network (ANN) was used to classify maize varieties that are grown under different agronomic management and to check the maize seeds, which are grown under the same agronomic management. Four classification set-ups were used, which are classification based on color features, morphology features, texture features and combination of color, texture and morphology features. For the analysis, 12 colors, 6- morphological and 12 textural features, totally 30 features were extracted from images of maize grains. Four experimental classification setups were applied. Setups 1, 2, 3 and 4 are classification designs by using color, morphology, texture and combination of morphology and color features, respectively. For all setups 160 images were used as inputs of the ANN. From this datasets, 70%(112) images were used for training the ANN while the remaining 30%(48) images were used for both testing and validation. The accuracy of classification using color, feature 99.4%, morphological feature was 87.5%, texture features was 100% and combination of morphological and color features was 87.5% for maize seeds.
