CLASSIFICATION OF RICE PLANT PESTS USING HAAR-LIKE FEATURE AND ADABOOST ALGORITHM

Abstract

Efforts to monitor pest populations at a rice plant site are important because based on
information on the type and number of pests attacking rice crops, a suggestion of
controlling can be developed early so that potential losses resulting from pests can be
suppressed. Therefore a process is needed to identify and classify the pests that attack
and harm the rice plants. In this research will be designed rice pest classification using
image processing where in its processing using image from stem borer (moth). Feature
extraction of positive samples (pest image of moths) and negative samples (non-pest
image) using Haar Like Feature. While in the process of classification into a class of
moths and not moths using Adaboost algorithm by applying cascade classifier to get a
strong characteristic. The observed variable is the error rate generated in the process of
pest classification of moth and non pest. From the test result on positive samples obtained
identification rate of true positive (TP) = 90%, while false positive (FP) = 20%. For
negative sample test (non pest image) obtained true negative (TN) = 80%, while false
negative (FN) = 20%. From the test result of positive sample and negative samples
obtained the accuracy of pest moth identification results of 85%