This paper presents a multi-class image segmentation approach to automate fruit segmentation. A feature learning algorithm combined with a conditional random field is applied to multi-spectral image data. Current classification methods used in agriculture scenarios tend to use hand crafted application-based features. In contrast, our approach uses unsupervised feature learning to automatically capture most relevant features from the data. This property makes our approach robust against variance in canopy trees and therefore has the potential to be applied to different domains. The proposed algorithm is applied to a fruit segmentation problem for a robotic agricultural surveillance mission, aiming to provide yield estimation with high accuracy and robustness against fruit variance. Experimental results with data collected in an almond farm are shown. The segmentation is performed with features extracted from multi-spectral (colour and infrared) data. We achieve a global classification accuracy of 88%.

This paper introduces an unsupervised graph cut based object segmentation algorithm, ShadowCut, for robotic aerial surveillance applications. By exploiting the spatial setting of the aerial imagery, ShadowCut algorithm differs from state-of-the-art object segmentation algorithms ([1] [2] [3] [4] [5]) by not requiring a large number of labelled training data set, nor constant user interaction ([6] [7] [8]). In this paper it is shown that, by combining robotic navigation data and a shadow model, it is possible to provide these seed labels with a probabilistic sampling model for object segmentation in aerial imagery. Experiments were performed on aerial data sets consisting of data collected in outback Australia with an aerial robotic platform during an ecological surveillance mission, and aerial images with various natural targets from Google Earth. The segmentation results from the unsupervised ShadowCut algorithm are shown to be comparable with those from supervised graph cut algorithms.