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

BioMed Central
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  • Methodology Article
  • Computer Science
  • Medicine

Abstract ral ss BioMed CentBMC Bioinformatics Open AcceMethodology article Machine learning techniques in disease forecasting: a case study on rice blast prediction Rakesh Kaundal1, Amar S Kapoor2 and Gajendra PS Raghava*1 Address: 1Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh 160036, India. and 2Department of Plant Pathology, CSK HPAU, Palampur HP 176062, India. Email: Rakesh Kaundal - [email protected]; Amar S Kapoor - [email protected]; Gajendra PS Raghava* - [email protected] * Corresponding author Abstract 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 0.50, which increased to 0.60 and percent mean absolute error (%MAE) decreased from 65.42 to 52.24 when back-propagation neural network (BPNN) was used. With generalized regression neural network (GRNN), the r increased to 0.70 and %MAE also improved to 46.30, which further increased to r = 0.77 and %MAE = 36.66 when support vector machine (SVM) based method was used. Similarly, cross-location validation achieved r = 0.48,

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