Forecasting anthracnose-twister disease using weather based parameters: geographically weighted regression focus

Isip Miguelito,Alberto Ronaldo,Biagtan Ariel 대한공간정보학회 2021 Spatial Information Research Vol.29 No.5

The aim of this study is to identify the environmental factors that may influence the onion anthracnose- twister disease incidence and severity. In this study, Geographically Weighted Regression (GWR) analysis was used to identify the dominant environmental factors that might influence the occurrence of Anthracnose-twister disease of onion using Geographic Information System approach. The onion disease records were acquired from the Institute of Climate Change and Environmental Management. The weather parameters such as relative humidity, cumulative rainfall and temperature were acquired from the National Aeronautics and Space Administration website while the river parameters were generated from Sentinel-2 images. This study has identified the ‘distance to river’ and ‘rainfall’ factor as the two (2) important factors that may influence the occurrence of the disease. The predictive surface map generated from GWR model was able to predict the occurrence of the disease in onion field by as much as 86% in the study area. The results of the study can be used to forecast the occurrence of anthracnose- twister disease in the onion fields the future.

Hot spot area analysis of onion armyworm outbreak in Nueva Ecija using geographic information system

R. T. Alberto,A. R. Biagtan,M. F. Isip,R. C. Tagaca 대한공간정보학회 2019 Spatial Information Research Vol.27 No.6

Onion is one of the high-value crops in the Philippines and Nueva Ecija is the leading onion producer in the country. Though onion is one of the most profitable high-value crops, it is very susceptible to armyworm infestations resulting in huge income loss to the farmers. To avoid losses in the future and expedite assistance by the government in the heavily infested areas, the pattern, spatial information and mapping of armyworm infestation and damage are information of vital importance. However, its importance is still far from realization by the farmers and decision makers, which at present are still depending on the traditional methods of estimating losses in yield/ha basis. Geographic Information System is the most advanced technology used in resources mapping which could also use in identifying heavily infested area through ‘‘Hot Spot Analysis’’. Hotspot analysis was conducted in this study to identify the spatial pattern and possible sources of armyworm infestation outbreaks. The result shows that the onion area of San Jose was classified as the highest hot spot area of armyworm infestation. On the other hand, the onion areas in the municipalities of Cuyapo, Guimba, San Leonardo, Rizal, General Natividad, Laur, and Bongabon, were also found to be high hot spot areas of infestation, though in moderate scale of damage. The municipalities of Lupao, Munoz, Gabaldon, Santo Domingo, Talavera, Quezon, and Aliaga, were found to be at very low to moderate hotspot status. GIS was proven to be effective in generating hot spot maps of armyworm based on the levels of infestation in the onion areas of Nueva Ecija.

Exploring vegetation indices adequate in detecting twister disease of onion using Sentinel-2 imagery

M. F. Isip,R. T. Alberto,A. R. Biagtan 대한공간정보학회 2020 Spatial Information Research Vol.28 No.3

Traditional plant disease detection is time consuming and costly, thus an inexpensive and faster alternative method of detection is needed to send early warning to farmers to prevent pests and disease infestation and for proper intervention. To provide timely and accurate detection in twister disease of onion, remote sensing was exploited using Sentinel 2 imageries. Vegetation indices (VIs) derived from the VIS–NIR region of the image were evaluated for their capability to detect twister disease. VIs were subjected to regression analysis to evaluate the relationship between vegetation indices and severity index of onion twister disease. Vegetation indices with strong relationship to twister disease were selected and further used in unsupervised ISODATA classification. Overall accuracy of classification generated from vegetation indices were calculated based on confusion matrix using ground truth points collected from field work to identify the most suitable index based on highest overall accuracy. It was found out that NDVI and GNDVI has the highest coefficient of determination (R2) indicating a strong relationship to the disease severity. Results of the classification shows that GNDVI, PSSRa and NDVI obtained the highest overall accuracy of 83.33%, 80.95% and 78.57% respectively. This indicates that these 3 VIs can be used for detection of twister disease in the field since it gives better discrimination and high accuracies. Hence, VI’s generated from Sentinel 2 imagery has the potential in detection, monitoring and management of twister disease of onion in the field.

Extraction of onion fields infected by anthracnose-twister disease in selected municipalities of Nueva Ecija using UAV imageries

R. T. Alberto,J. C. E. Rivera,A. R. Biagtan,M. F. Isip 대한공간정보학회 2020 Spatial Information Research Vol.28 No.3

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.

Disease risk map of anthracnose-twister of onion based on previous disease locations as a future predictors

R. T. Alberto,M. F. Isip,A. R. Biagtan,R. C. Tagaca 대한공간정보학회 2019 Spatial Information Research Vol.27 No.3

Understanding the disease epidemiology of anthracnose-twister disease provide us with information about the spread of disease in different areas with different climates which necessitates site specific disease predictions, management and spread of infection to other areas. Anthracnose-twister disease is caused by Colletotrichum gloeosporioides and Gibberella moniliformis and is considered to be the most destructive disease of onion in the Philippines. The disease had spread in Nueva Ecija and neighboring onion growing provinces in Luzon. To prevent the same situation in the future, disease risk maps could be of great value among decision makers and farmers to minimize damage and losses due to the disease. A geographic information system is an essential tool in analyzing disease data associated with geographic locations which can generate spatial distribution, spread and occurrence of plant diseases in the form of maps. These can provide meaningful information that can be easily interpreted. In this study, the data of previous disease location was utilized to generate prediction and disease risk maps through interpolation using Kriging model. Based on the results, the prediction map suggests anthracnose-twister disease of onion will become an epidemic and the disease outbreak will most likely to occur in the southern part of Bongabon (Philippines). It shows that the southeastern part of Bongabon has a very high risk due to the high incidence rate (50.01% to 75.00%) on this area during the previous cropping seasons. To mitigate the situation in these areas it is recommended to avoid using white onion varieties which is very susceptible to anthracnose-twister, and spray potential fungicides 1 week after transplanting.