Analysis of Agricultural Features

Abstract

In agriculture field, classification of agricultural plants is a major problem due to need for improving the crop yield. This research work focuses on the classification of crops by applying machine vision and knowledge-based techniques with image processing by using different feature descriptors including texture, color, HOG (Histogram of oriented gradients) and GIST (Global image descriptor). A combination of all these features was used in the classification of crops. In this research, several machine learning algorithms including both base classifiers and ensemble classifiers were applied and the performances of classification results were evaluated by majority voting. Naive Bayes (NB), Support Vector Machine (SVM), K-nearestneighbor (KNN) and Multi-Layer Perceptron (MLP) were used as Base classifiers. Ensemble classifiers include Random Forest (RF), Bagging and Adaboost were utilized. The experimental results showed that the classification accuracy is improved by majority voting with ensemble classifiers in the combination of texture, color, HOG and GIST features.