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UAS based Tomato Yellow Leaf Curl Virus (TYLCV) disease detection system
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Paper Abstract

Tomato production faces constant pressure of biotic and abiotic stresses that can cause significant loss of production and fruit quality. In tropical and subtropical climates, the main disease affecting tomato production is caused by Tomato Yellow Leaf Curl Virus (TYLCV), a virus that is vectored by the silverleaf whitefly (Bemisia tabaci). The main method of control relies on insecticide spray to control the vector, avoiding the spread of the disease. Detecting and spatially locating infected plants are required to prevent and control epidemic outbreak of TYLCV. In this study, we aim to develop an unmanned aircraft system (UAS) based TYLCV detection algorithm that can identify affected plants and provide physiological information of the affected plants. Multi-temporal phenotypic attributes, e.g., canopy height, canopy cover, canopy volume, and vegetation indexes including normalized difference vegetation indexes (NDVI), soil adjusted vegetation index (SAVI), and excess green index (ExG) were extracted from the UAS image data. The field experiment was conducted at Texas A and M Agrilife Research and Extension Center at Weslaco, TX. A total of 16 tomato hybrids with different levels of TYLCV resistance were inoculated with viruliferous insects and randomly transplanted in open field with triplicates plots containing 4 plants. One control plot for each tomato hybrid with non-inoculated plants were also planted for validation. Machine learning techniques based on artificial neural networks were used to detect TYLCV symptoms in plants from UAS-driven parameters, and all the plants were tested by polymerase chain reaction (PCR) using specific primers to confirm TYLCV infection. To evaluate how early and accurately the algorithm can detect TYLCV symptoms in tomato plants, various detection models were developed by changing the period of input UAS data. We expect that the suggested system to be a useful framework for monitoring outbreak of TYLCV in large scales, giving the ability for the grower to determine the best time and location to start the vector control and also generate time series physiological data for better understanding of the disease progression.

Paper Details

Date Published: 14 May 2019
PDF: 7 pages
Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 110080P (14 May 2019); doi: 10.1117/12.2518703
Show Author Affiliations
Sungchan Oh, Texas A&M Univ. Corpus Christi (United States)
Akash Ashapure, Texas A&M Univ. Corpus Christi (United States)
Thiago G. Marconi, Texas A&M AgriLife Research and Extension Ctr. (United States)
Jinha Jung, Texas A&M Univ. Corpus Christi (United States)
Juan Landivar, Texas A&M AgriLife Research and Extension Ctr. (United States)

Published in SPIE Proceedings Vol. 11008:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

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