A Bayesian classification approach for predicting Gesonia gemma Swinhoe population on soybean crop in relation to abiotic factors based on economic threshold level

Abstract

Predicting of insect pest population with accuracy and speed when given large data set will make a major contribution to the success of integrated pest management. Naïve Bayesian classification has been proposed for predicting the insect pest Gesonia gemma Swinhoe on soybean crop. The Naïve Bayesian classifier works based on Bayes’ theorem and can predict class probabilities that a given tuple from the dataset belongs to a particular class. The dataset includes abiotic factors as features along with the class feature (pest incidence) are separated as training data and testing data, then the model was built on the training set by finding the probability for each of its features in relation with the class feature. The Naïve Bayesian classification from the trained model, best fits the testing data with 90% accuracy, thus the proposed approach can be very useful in predicting the pest G. gemma on soybean crop.