The aim of this research is to develop a reliable tool by a special method, combining image processing based on watershed algorithm, and a predictive model to estimate automatically the flowers number per inflorescence. Eighty images of Vitis vinifera L. inflorescence (the Cardinal cultivar) were processed. Watershed algorithm was used for the image processing and this was followed by statistical analysis that provides robust predictive estimation of the flower button number. The results show a robust estimation, compared to manual flowers counting, with strong correlation. The developed algorithm shows that the watershed algorithm was able to provide an automatic assessment of the flower button number in the inflorescence. The method used is more robust and provides a more significant level compared with recent studies. In the applied research in viticulture, it is crucial to improve knowledge of yield forecasting and to study the fruit set rates estimation. The technique is used to determine, with a higher significance level, the fruitiness rate of grapevine at the early stage of flowering.
Extraction of feature and classification methods are important phases in recognition system. A good classifier and extraction of features that suits play a very important role in a recognition system to improving recognition rate. In this paper we propose a new system designed to recognize the ten digits of printed Arabic numerals that are the most common symbolic representation of numbers in the world today. This study has been conducted using Hu moment, number of hole and surface which are tolerate to the geometric transformations along with seven different classifiers Naive Bayesian, Multi-Layer Perceptron (MLP), Linear Discriminant Analysis (LDA), Pseudo-Inverse. Support Vector Machine (SVM), Decision Tree and K-Nearest Neighbor (KNN). Classifier combination is considered. Experimental tests demonstrated that our technique achieves good results on multi-font and multi-size printed digit dataset.
the aim of this paper is to describe the combining of several classifiers to the recognition of printed digits using a novel approach to describe the digits by hybrid feature extraction. The study has been conducted using three different features computed from cavities, zonal extraction and retinal representation along with nine different classifiers , K-Nearest Neighbor -KNN-with different distance measure, Support Vector Machine -SVM-, decision tree, linear discriminant analysis -LDA-. Classifier combination is considered by Majority Voting method. Experimental tests carried on the multi-font and multi-size printed digits dataset.
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