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

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 cros

SUBMITTER: Kaundal R 

PROVIDER: S-EPMC1647291 | biostudies-literature | 2006 Nov

REPOSITORIES: biostudies-literature