Robotic Harvesting System for Eggplants

Abstract

The harvesting operation for eggplants is complicated and accounts for a little less than 40% of the total number of working hours. For automating the harvesting operation, an intelligent robot that can emulate the judgment of human labor is necessary. This study was conducted with a view to developing a robotic harvesting system that performs recognition, approach, and picking tasks. In order to accomplish these tasks, 3 essential components were developed. First, a machine vision algorithm combining a color segment operation and a vertical dividing operation was developed. The algorithm could detect the fruit even under different light conditions. Next, a visual feedback fuzzy control model to actuate a manipulator was designed. The control model enabled the manipulator end to approach the fruit from a distance of 300 mm. Furthermore, an end-effector composed of a fruit-grasping mechanism, a size-judging mechanism, and a peduncle-cutting mechanism was developed. It produced enough force for grasping the fruit and cutting the tough peduncle. Finally, the 3 essential components were functionally combined, and a basic harvesting experiment was conducted in the laboratory to evaluate the performance of the system. The system showed a successful harvesting rate of 62.5%, although the end-effector cut the peduncle at a slightly higher position from the fruit base. The execution time for harvesting of an eggplant was 64.1 s.

Hierarchical classification of robotic grippers applied for agricultural object manipulations

“…Three-finger gripper with video camera for citrus harvesting [13] Vacuum gripper with a video camera for Harvesting tomatoes [15] Six-finger pneumatic gripper with video camera to harvesting eggplants [16] Two-finger grip with pressure and collision sensors for harvesting apple [17] Two-fingered permanent gripper with a cutting knife and a video camera for removing tomato leaves [18] …”

Fuzzy classification of pre-harvest tomatoes for ripeness estimation – An approach based on automatic rule learning using decision tree

“…Color spaces commonly used by harvesting robots include RGB [18], HSI [19], L*a*b* and others. Statistical Hue feature of HSI color space is generally used but the disadvantage of variation in illumination is associated with this color space.…”

Review. Technologies for robot grippers in pick and place operations for fresh fruits and vegetables

“…Sarig (1993) reviewed state-of-the-art of robotics in harvesting, pointing out the necessities to optimize R&D work required for the realization of robotic harvesting. Achievements in fruit and vegetable handling have been found in picking harvesting systems for products such as: strawberries (Hayashi et al, 2010), cucumbers (Van Henten et al, 2009), tomatoes (Monta et al, 1998a,b;Ceccarelli et al, 2000), aubergines (Hayashi et al, 2002), apples (Setiawan et al, 2004), chicories (Foglia and Reina, 2006), and oranges (Muscato et al, 2005).…”