UAS Based Tomato Yellow Leaf Curl Virus (TYLCV) Disease Detection System

摘要

Tomato production faces constant pressure of biotic and abiotic stresses that can cause significant loss of production andfruit quality. In tropical and subtropical climates, the main disease affecting tomato production is caused by Tomato YellowLeaf Curl Virus (TYLCV), a virus that is vectored by the silverleaf whitefly (Bemisia tabaci). The main method of controlrelies on insecticide spray to control the vector, avoiding the spread of the disease. Detecting and spatially locating infectedplants are required to prevent and control epidemic outbreak of TYLCV. In this study, we aim to develop an unmannedaircraft system (UAS) based TYLCV detection algorithm that can identify affected plants and provide physiologicalinformation of the affected plants. Multi-temporal phenotypic attributes, e.g., canopy height, canopy cover, canopyvolume, 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 atTexas A&M Agrilife Research and Extension Center at Weslaco, TX. A total of 16 tomato hybrids with different levels ofTYLCV resistance were inoculated with viruliferous insects and randomly transplanted in open field with triplicates plotscontaining 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 fromUAS-driven parameters, and all the plants were tested by polymerase chain reaction (PCR) using specific primers toconfirm TYLCV infection. To evaluate how early and accurately the algorithm can detect TYLCV symptoms in tomatoplants, various detection models were developed by changing the period of input UAS data. We expect that the suggestedsystem to be a useful framework for monitoring outbreak of TYLCV in large scales, giving the ability for the grower todetermine the best time and location to start the vector control and also generate time series physiological data for betterunderstanding of the disease progression.