Digital Disease Phenotyping

Cristhian Delgado, Hernan Benitez, Maribel Cruz, Michael Selvaraj

Producción: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

4 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas5702-5705
Número de páginas4
ISBN (versión digital)9781538691540
DOI
EstadoPublicada - jul. 2019
Evento39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japón
Duración: 28 jul. 201902 ago. 2019

Serie de la publicación

NombreInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conferencia

Conferencia39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
País/TerritorioJapón
CiudadYokohama
Período28/07/1902/08/19

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