Rice Blast Disease Forecasting for Northern Philippines

Rice blast disease has become an enigmatic problem in several rice growing ecosystems of both tropical and temperate regions of the world. In this study, we develop models for predicting the occurrence and severity of rice blast disease, with the aim of helping to prevent or at least mitigate the spread of such disease. Data from 2 government agencies in selected provinces from northern Philippines were gathered, cleaned and synchronized for the purpose of building the predictive models. After the data synchronization, dimensionality reduction of the feature space was done, using Principal Component Analysis (PCA), to determine the most important weather features that contribute to the occurrence of the rice blast disease. Using these identified features, ANN and SVM binary classifiers (for prediction of the occurrence or non-occurrence of rice blast) and regression models (for estimation of the severity of an occurring rice blast) were built and tested. These classifiers and regression models produced sufficiently accurate results, with the SVM models showing a significantly better predictive power than the corresponding ANN models. These findings can be used in developing a system for forecasting rice blast, which may help reduce the occurrence of the disease

Rice Blast Disease Forecasting for Northern Philippines

Abstract: -Rice blast disease has become an enigmatic problem in several rice growing ecosystems of both tropical and temperate regions of the world. In this study, we develop models for predicting the occurrence and severity of rice blast disease, with the aim of helping to prevent or at least mitigate the spread of such disease. Data from 2 government agencies in selected provinces from northern Philippines were gathered, cleaned and synchronized for the purpose of building the predictive models. After the data synchronization, dimensionality reduction of the feature space was done, using Principal Component Analysis (PCA), to determine the most important weather features that contribute to the occurrence of the rice blast disease. Using these identified features, ANN and SVM binary classifiers (for prediction of the occurrence or non-occurrence of rice blast) and regression models (for estimation of the severity of an occurring rice blast) were built and tested. These classifiers and regression models produced sufficiently accurate results, with the SVM models showing a significantly better predictive power than the corresponding ANN models. These findings can be used in developing a system for forecasting rice blast, which may help reduce the occurrence of the disease

CROWDSOURCING APPROACH FOR DISASTER RESPONSE ASSESSMENT

Philippines are a country attuned to social media and a disaster prone country and recent research focused on the interesting use of Twitter. This work is motivated to provide information through crowdsourcing, which uses humans as sensors to observe and report events in the physical world. In this paper we propose that, Twitter feeds which consist of short messages to extract information as a tool in needs assessment for a disaster hit community. This information will serve as situation awareness through crowd sensing, in order to deliver the relevant basic needs to the disaster stricken community and humanitarian disaster response. The data were obtain using the Twitters open search API, preliminary experiment is carried out, Naïve Bayes algorithm was used to classify disaster related tweets. The geo location feature in the tweets were extracted and translated into map for visualization and the information related to disaster. This study will be helpful in identifying, analyzing, monitoring and evaluating basic needs of the affected communities, in order for the decision makers to take necessary actions and respond to the needs of the people