Computer Vision based System for Apple Detection in Crops

In recent times there has been an increasing need to improve apple production competitiveness. The automatic estimation of the crop yield or the automatic collection may contribute to this improvement. This article proposes a simple and efficient approach to automatically detect the apples present on a given set of images. We tested the proposed algorithm on several images taken on many different apple crops under natural lighting conditions. The proposed method has two main steps. First we implement a classification step in which each pixel is classified as part of an apple (positive pixel) or as part of the background (negative pixel). Then, a second step explore the morphology of the set of positive pixels, to detect the most likely configuration of circular structures. We compare the performance of methods such as: Support Vector Machine, k-Nearest Neighbor and a basic Decision Tree on the classification step. A database with 266 high resolution images was created and made publicly available. This database was manually labeled and we provide for each image, a label (positive or negative) for each pixel, plus the location of the center of each apple.