Visual Feedback Guided Robotic Cherry Tomato Harvesting

Abstract

Harvesting cherry tomatoes is more laborious than harvesting larger size tomatoes because of the high fruit density in every cluster. To save labor costs, robotic harvesting of cherry tomatoes has been studied in Japan. An effective vision algorithm, to detect positions of many small fruits, was developed for guidance of robotically harvested cherry tomatoes. A spectral reflectance in the visible region was identified and extracted to provide high contrast images for the fruit cluster identification. The 3-D position of each fruit cluster was determined using a binocular stereo vision technique. The robot harvested one fruit at a time and the position of the next target fruit was updated based on a newly acquired image and the latest manipulator position. The experimental results showed that this visual feedback control based harvesting method was effective, with a success rate of 70%.