TY - GEN
T1 - Towards autonomous phytopathology
T2 - 2016 IEEE International Conference on Robotics and Automation, ICRA 2016
AU - Sarkar, Suproteem K.
AU - Das, Jnaneshwar
AU - Ehsani, Reza
AU - Kumar, Vijay
N1 - Funding Information:
We gratefully acknowledge the support of USDA grant 2015-67021-23857 under the National Robotics Initiative, ONR grant N00014-07-1-0829, NSF grant IIP-1113830, and the Berkman Opportunity Fund at the University of Pennsylvania. We also acknowledge Geraldine Lavin and Tracylea Byford at the University of Pennsylvania Greenhouse for supporting initial sensor trials and maintaining the citrus trees
Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/8
Y1 - 2016/6/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84977488477&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84977488477&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2016.7487719
DO - 10.1109/ICRA.2016.7487719
M3 - Conference contribution
AN - SCOPUS:84977488477
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 5143
EP - 5148
BT - 2016 IEEE International Conference on Robotics and Automation, ICRA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 May 2016 through 21 May 2016
ER -