Technical Note: Ripe Tomato Detection for Robotic Vision Harvesting Systems in Greenhouses

Abstract

Effective recognition and localization of ripe tomatoes from a complex background is the key issue for robotic harvesting systems in greenhouses. In this study, a detection approach for ripe tomatoes is proposed based on their color and shape features. Images containing ripe tomatoes are first segmented by K-means clustering using the L*a*b* color space. To recognize a single ripe tomato, mathematical morphology is used to denoise the image and to handle the situations of image overlapping and sheltering. To improve the accuracy of tomato detection, the shape features are combined with the color features. Finally, the center of the detected single ripe tomato is calculated. Experimental results demonstrate the effectiveness of the proposed method. The successful detection rate is approximately 94%. Even with the overlapping and complex cluster background, the proposed algorithm still shows a very strong robustness.