Automatic Feature Detection and Classification for Watermelon (Citrillus lanatus)

Abstract

This document focuses on the contributions made in the development and advances achieved in the task of automatic Feature Detection for Watermelon (Citrillus lanatus). A special interest is given to feature-based methods such as: morphological and adaptive threshold approaches, that work by extracting color and texture information. A first hand example about how these two methods can be applied to a data set comprised in export level watermelons coming from Panama is provided. Limitations of the method are discussed and a final conclusion about the field and recent avenues of work with ensemble methods is given. The importance of this document is that it helps the automatic understanding of watermelon patterns with computer vision.