Extraction of onion fields infected by anthracnose-twister disease in selected municipalities of Nueva Ecija using UAV imageries
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Remote sensing is one of the advanced technologies that can be used in early detection, mapping and spatial tracking of pests and disease infestations. This technology can give an updated information on the geoinformation and plant health status of the areas by conducting image analysis and classification processes using imageries captured by satellites and unmanned aerial vehicles (UAV). Anthracnose-twister disease is one of the destructive diseases of onion in the Philippines caused by fungi Colletotrichum gloeosporioides and Gibberella moniliformis. The manifestations of this disease in onion areas are very visible in aerial imageries captured by UAV’s, thus, these imageries were utilized in extracting infected onion areas in the fields. To map out the affected areas, object based image analysis (OBIA) was carried out using aerial imageries captured by the UAV’s. Vegetation indices generated from the Red, Green, Red Edge, and NIR bands were used as image layers and the support vector machine (SVM) as the classifier. The SVM was used to generate geophytopathological maps showing the actual picture and health status of onion fields with 85+% accuracy. The OBIA using SVM was effective in extracting infected onion areas using different vegetation indices, thereby, creating geophytopathological maps pin pointing the infected and the non-infected fields in the areas. These, maps were turned over to the decision makers and extension workers to raise the level of awareness on the infestation and used as monitoring tool in disease spread prevention as well as in planning for disease and pesticide management and environmental protection.
KeywordsSupport vector machine Image classification Segmentation Vegetation Indices
This research is an output of the Project Titled “Surveillance, Detection and Mapping of Leaf Miner and Anthracnose-Twister Disease of Onion and Garlic in Nueva Ecija”. We are grateful to the Department of Science and Technology (DOST) for the financial support and The Philippine Council for Agriculture, Aquatic, and Natural Resources Research and Development (PCAARRD) for managing and monitoring the project.
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