The devastating disease Huanglongbing (HLB) has greatly affected citrus in Florida and other growing regions. Detecting dropped fruit is one method of estimating the presence and severity of the disease. The purpose of this study was to develop a machine vision system that can detect dropped citrus on the ground in varying illumination conditions and identify decaying stages of the dropped fruit. In this paper, a novel method for image brightness correction using a contrast limited adaptive histogram equalization was developed to produce constant image brightness levels between and within images. Objectives of this study were to: (1) solve the varying illumination problem and create a consistent brightness level between and within the images, (2) develop an algorithm to eliminate multiple detections of a single fruit from the circular Hough transform, (3) design an algorithm to evaluate decaying stages of the dropped citrus, and (4) demonstrate ability to create a fruit drop map of citrus at each decaying stage in a commercial citrus grove. The result shows all processed images had desired brightness levels (152 out of 255) with a standard deviation of 1.0. Correct identification of fruit and false positives were measured as 89.6% and 5.0%, respectively. False classifications of decay stages of fruit were as low as 4.2% and 18.5% for recently dropped fruit and rotten fruit, respectively. The techniques developed in this work could be further developed into a commercial machine vision system for a real-time dropped fruit mapping system.
All Science Journal Classification (ASJC) codes
- Agronomy and Crop Science
- Computer Science Applications