  Apple is one of the remarkable fresh fruit that contains a high degree of
nutritious and medicinal value. Hand harvesting of apples by seasonal
farmworkers increases physical damages on the surface of these fruits, which
causes a great loss in marketing quality. The main objective of this study is
focused on designing a robust vision algorithm for robotic apple harvesters.
The proposed algorithm is able to recognize and classify 4-classes of objects
found in an orchard scene including apples, leaves, trunk and branches, and sky
into two apples and non-apples classes. 100 digital images of Red Delicious
apples and 100 digital images of Golden Delicious apples were selected among
1000 captured images of apples from 18 apple gardens in West Azerbaijan, Iran.
An image processing algorithm is proposed for segmentation and extraction of
the image classes based on the color characteristics of mentioned classes.
Invariant-Momentums were chosen as the extracted features from the segmented
classes, e.g. apples. Multilayer Feedforward Neural Networks, MFNNs, were used
as an artificial intelligence tool for the recognition and classification of
image classes.
