Statistical Based Real-Time Selective Herbicide Weed Classifier

Abstract

This paper deals with the development of an algorithm for real time specific weed recognition system based on Sample Variance of an image that is used for the weed classification and comparison of its result with the algorithm based on population variance. The population variance has been used before for weed classification. The processing time for calculating population variance and sample variance for different samples is given. This algorithm is specifically developed to classify images into broad and narrow class for real-time selective herbicide application. The developed system has been tested on the weeds in the lab along with the prior algorithm based on population variance, which have shown that the system is very effective in weed identification and efficient than the algorithm based on population variance. Further the results show a very reliable performance on images of weeds taken under varying field conditions. The analysis of the results shows over 97 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds. The algorithm developed in this paper has improved efficiency.