Climate Based Factor Analysis and Epidemiology Prediction for Potato Late Blight Using Machine Learning Approaches

Abstract

Potato is one of the largest food crop and an integral part of world’s food supply. Late Bligh in Potato is community disease and has capability to devastate the entire crop rapidly. Estimated average annual loss form PLB is 15% around the world. In presented work, the task of Factor Analysis and epidemiology prediction are assigned to SVM and ELM respectively for Potato Late Blight. Factor Analysis Model calculate the weights of the Climate based parameters depending on their relevance in deciding the blight and blight free year. The feature subset selected using SVM are used as input to ELM for epidemiology prediction along with the age of the plant. Two databases are prepared from AICRP and Climate Data, one for Factor Analysis and one for Epidemiology prediction with five class labels (1-5). Database for Epidemiology Prediction is further divided into three sub databases for three separate planting dates. Analysis of the experimental results for Factor analysis shows that Maximum temperature, Maximum and Minimum Humidity, Sun Shine hours and Evaporation plays major role in occurrence of late blight disease. Experiments are conducted for Epidemiology Prediction with other activation functions and different partitions of database. On the basis of obtained results, SinC activation Function outperformed sigmoid function and has promising accuracy for all the data partitions.