A Vision Based Sensor for Huanglongbing Disease Detection under a Simulated Field Condition

Abstract

Florida citrus industry has been suffering from an extremely serious infection, called Huanglongbing (HLB) or citrus greening disease for several years. The disease has been seen in all citrus producing counties in Florida and also some infection has been recently reported in Texas and California. No effective cure has been discovered for this disease yet, still a quick and easy HLB detection method can help citrus growers to remove the infected trees and protect the rest of their groves. Starch accumulation on an infected citrus leaf is one of the HLB symptoms, which create some green islands and yellowish blotch mottles on the leaf. In this study, a new version of a machine vision based sensor was developed which is able to identify the HLB symptomatic leaves under simulated field conditions. The combination of a sensitive monochrome camera and a narrow band polarized illumination system in this sensor could highlight the starch accumulation in HLB infected citrus leaves and differentiate it from yellowish leaves caused by other stresses such as nutrient deficiency. This sensor was examined in both simulated and actual in-field conditions for HLB symptomatic, healthy and nutrient (zinc) deficient leaf samples. Simple statistical histogram features were used for the classification and classification accuracies of over 95% were achieved. Compared to the previous version of our HLB detection system, this approach improved the detection accuracy with a less complicated classification method.