Orchard fruit segmentation using multi-spectral feature learning

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%.

Authors

• Calvin Hung
• Juan I. Nieto 0001
• Zachary Taylor
• James Patrick Underwood
• Salah Sukkarieh

Keywords

• Image segmentation
• Feature extraction
• Image color analysis
• Training
• Accuracy
• Robots
• Shape
• Feature Learning
• Fruit Segmentation
• Unsupervised Learning
• Multispectral Images
• Tree Canopy
• Segmentation Approach
• Yield Estimation
• Conditional Random Field
• Segmentation Problem
• Global Accuracy
• Unsupervised Feature Learning
• Increase In Production
• F1 Score
• Descriptive Characteristics
• Average Accuracy
• Object Detection
• Multinomial Regression
• Normalized Difference Vegetation Index
• Graphical Model
• Semi-supervised Learning
• RGB Channels
• Sparse Autoencoder
• Multi-scale Features
• Enhanced Vegetation Index
• Segmentation Results
• Entire Training Dataset
• Segmentation Algorithm
• Extra Channels
• Pairwise Potential