Machine learning techniques in disease forecasting: a case study on rice blast prediction

Background: Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown data points and longer training times, there is need for exploiting new prediction softwares for better understanding of plant-pathogen-environment relationships. Further, there is no online tool available which can help the plant researchers or farmers in timely application of control measures. This paper introduces a new prediction approach based on support vector machines for developing weather-based prediction models of plant diseases. Results: Six significant weather variables were selected as predictor variables. Two series of models (cross-location and cross-year) were developed and validated using a five-fold cross validation procedure. For cross-year models, the conventional multiple regression (REG) approach achieved an average correlation coefficient (r) of 0.5...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Rakesh Kaundal, Amar S. Kapoor, Gajendra P. S. Raghava
• Keywords: BMCBI 2006, Neural Network, Prediction, Support Vector Machine