Classification of arecanut using machine learning techniques

In agricultural domain research, image processing and machine learning techniques play an important role. This paper provides a unique solution for classifying the good defective arecanuts based on their color, texture, density value. market different varieties of arecanut are available. Usually, qualitative sorting is done manually, this can be replaced by applying vision to grade arecanut. Classification quality using various it observed that artificial neural networks give results compared other classifiers like logistic regression, <em>k</em>-nearest neighbor, naive Bayes classifiers, support vector machine. A feature considered here better classification. The result without considering with respect work than others. proposed method works effectively accuracy 98.8%.