Towards autonomous phytopathology: Outcomes and challenges of citrus greening disease detection through close-range remote sensing

Abstract

Unmanned aerial vehicles (UAVs) have the potential to significantly impact early detection and monitoring of plant diseases. In this paper, we present preliminary work in developing a UAV-mounted sensor suite for detection of citrus greening disease, a major threat to Florida citrus production. We propose a depth-invariant sensing methodology for measuring reflectance of polarized amber light, a metric which has been found to measure starch accumulation in greening-infected leaves. We describe the implications of adding depth information to this method, including the use of machine learning models to discriminate between healthy and infected leaves with validation accuracies up to 93%. Additionally, we discuss stipulations and challenges of use of the system with UAV platforms. This sensing system has the potential to allow for rapid scanning of groves to determine the spread of the disease, especially in areas where infection is still in early stages, including citrus farms in California. Although presented in the context of citrus greening disease, the methods can be applied to a variety of plant pathology studies, enabling timely monitoring of plant health-impacting scientists, growers, and policymakers.