TY - GEN
T1 - Digital Disease Phenotyping
AU - Delgado, Cristhian
AU - Benitez, Hernan
AU - Cruz, Maribel
AU - Selvaraj, Michael
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Precise and rapid methods of plant disease detection and evaluation are key factors to accelerate resistant variety development in the rice breeding program. Conventional methods for the disease detection and evaluation is mainly carried out using standard visual estimation by trained experts which is slow and prone to high level of subjectivity. Rigorous research has recently recognized innovative, sensor-based methods for the detection and evaluation of plant diseases. Among different type of sensors, aerial multispectral imaging provides a fast and nondestructive way of scanning plants in diseased regions and has been used by various researchers to classify symptom levels on the spectral profile of a plant. In this paper, we developed machine learning models to classify rice breeding lines infected by rice Hoja Blanca virus (RHBV) using multispectral images collected from UAV (unmanned aerial vehicle). Our results revealed that, the Support Vector Machine (SVM) and Random Forest (RF) methods were not significantly different in their ability to separate susceptible from non-susceptible classes, but SVM best classifiers showed a better sensitivity rates 0.74 (SVM) versus 0.71 (RF). The tool developed from this study will allow rice breeders to characterize Hoja Blanca virus resistant varieties considerably earlier, and subsequent in reduced costs.
AB - Precise and rapid methods of plant disease detection and evaluation are key factors to accelerate resistant variety development in the rice breeding program. Conventional methods for the disease detection and evaluation is mainly carried out using standard visual estimation by trained experts which is slow and prone to high level of subjectivity. Rigorous research has recently recognized innovative, sensor-based methods for the detection and evaluation of plant diseases. Among different type of sensors, aerial multispectral imaging provides a fast and nondestructive way of scanning plants in diseased regions and has been used by various researchers to classify symptom levels on the spectral profile of a plant. In this paper, we developed machine learning models to classify rice breeding lines infected by rice Hoja Blanca virus (RHBV) using multispectral images collected from UAV (unmanned aerial vehicle). Our results revealed that, the Support Vector Machine (SVM) and Random Forest (RF) methods were not significantly different in their ability to separate susceptible from non-susceptible classes, but SVM best classifiers showed a better sensitivity rates 0.74 (SVM) versus 0.71 (RF). The tool developed from this study will allow rice breeders to characterize Hoja Blanca virus resistant varieties considerably earlier, and subsequent in reduced costs.
KW - Hight Troughtput Phenotyping (HTP)
KW - Machine Learning (ML)
KW - Rice Hoja Blanca Virus (RHBV)
KW - Unmaned Aerial Vehicle (UAV)
UR - http://www.scopus.com/inward/record.url?scp=85077713413&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2019.8897854
DO - 10.1109/IGARSS.2019.8897854
M3 - Conference contribution
AN - SCOPUS:85077713413
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 5702
EP - 5705
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -