Digital image analysis based automated kiwifruit counting technique

Abstract

This paper investigates the use of digital image analysis techniques for developing an automated kiwifruit counting system. Three simple counting methods followed by a minimum distance classifier based segmentation technique in L*a*b colour space were studied. Images were taken prior to harvesting at a New Zealand kiwifruit orchard. Accurate counting of kiwifruit in several sample regions of the orchard is required in order to estimate the fruit harvest. At present, the counting is manually done by hired employees. Manual counting has several issues, such as low accuracy, long duration and higher costs. Automated counting technique facilitates a fast, low cost and potentially more accurate way of counting kiwifruit. Several approaches were trialled and validated on different sets of images. Above 90% accuracy on gold image data and above 60% accuracy on green image data were obtained, showing the potential of using the approach for counting kiwifruit for the harvest estimation purpose. The results, limitations and ongoing research in developing a more robust and consistent technique will be discussed.