Abstract
Machine Learning is one among the current topic that is under research in different domains. It plays a vital role in agriculture domain also. Automatic harvesting and grading of crops and fruits is one of the main confront in the agricultural process. The key technology used in fruit recognition and yield counting is intelligent-detection system, where image-processing techniques can be implemented. According to the existing works the KNN is the popular machine learning algorithm that can be implemented in this system. The aim of this paper is to develop a framework with this algorithm in fruit classification which includes three phases namely pre- processing, feature extraction, and classification. The aim this framework is to provide an effective, simple, computer vision mechanism to classify type of the fruits and the ripen stage of the fruits like apple, mango, orange, watermelon and pomegranate.
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