Abstract of Thesis Presented to the Graduate School
 of the University of Florida in Partial Fulfillment of the
 Requirements for the Degree of Master of Science

 CITRUS YIELD MAPPING SYSTEM USING MACHINE VISION

 By

 Palaniappan Annamalai

 August 2004

Chair: Won Suk "Daniel" Lee
Maj or Department: Agricultural and Biological Engineering

 A machine vision system utilizing color vision was investigated as a means to

identify citrus fruits and to estimate yield information of the citrus grove in real-time. The

yield mapping system was calibrated and tested in a commercial citrus grove. Results

were compared for the yield estimated through the system and that carried out by hand

harvesting.

 This study focused on three major issues:

1. Development of a hardware system consisting of a color CCD camera, an imaging
 board, an encoder and a DGPS receiver;
2. Development of an algorithm to take non-overlapping images of the citrus grove
 and an image-processing algorithm to identify and count the number of citrus fruits
 from an image;
3. Development of a yield estimation model that will predict the number of fruits per
 tree based on the images of the tree.

 Images were acquired for 98 citrus trees in a grove located near Orlando, Florida.

The trees were distributed over 48 plots evenly. Images were taken in stationary mode

using a machine vision system consisting of a color analog camera, a DGPS receiver, and

an encoder. Non-overlapping images of the citrus trees were taken by determining the