This paper focuses on maturity evaluation derived by a color camera for a sweet pepper robotic harvester. Different color and morphological features for sweet pepper maturity were evaluated. Side view and bottom view of sweet paper were analyzed and compared for their ability to classify into 4 maturity classes. The goal of this study was to differentiate between the two center classes which are difficult to separate. Statistical analysis of 13 different features in reliance to the maturity classification and the views indicated the best features for classification. The results show that the features that can be used for classification between the two central classes from both bottom and side views are: Hue range, Equal2Real – the ratio between the equivalent equal sized circle perimeter to the shape perimeter and Area2Peri – the ratio between the area to the perimeter.

A prototype robot for picking citrus is described which utilized real-time, color machine vision to vision-servo the robot on a targeted fruit. A programming technique is presented which simplified development of the task-level, robot control program. An economic evaluation of robotic harvesting in Florida determined that robotic harvesting would be approximately 50 percent more expensive than conventional hand harvesting. Harvest inefficiency was identified as the most influential factor affecting robotic harvesting economics. Field trial performance is summarized and the potential of commercial robotic harvesting discussed.