Gesonia gemma Swinhoe (1885) is a grey semi-looper and it has emerged as a serious threat to the soybean crop. This defoliator causes heavy damage to the crop in the form of loss in grain weight. Gesonia gemma population dynamics was studied in various districts of Maharashtra. Sequential covering algorithm (CN2 rule induction) has been proposed for rule induction model to generate a list of classification rules with target feature (G. gemma population) and the independent abiotic features. The classification rules have exhibited more accuracy and showed that maximum temperature and humidity with less number of rainy days has influenced the population of Gesonia gemma in Maharashtra. Hence, this rule induction model can be used to study the collected evidence for prediction and it will be helpful to the farmers to take necessary pest control strategy.
Efficient Sequential Covering Strategy for Classification Rules Mining using a Discrete Equilibrium Optimization Algorithm
