Performance Comparison of Weed Detection Algorithms

Weed control is essential in agricultural productivity as weeds act as a pest to crops. The conventional methods of weed removal are time-consuming and require more manual labour work. Hence there is a need to automate this process. The objective of the proposed system is to detect weed from crop using machine learning algorithms. The exhaustive dataset is collected for four different commercial crops and two types of weeds such as Para grass and Nutsedge. Excess green method and Otsu's thresholding is used for masking the soil and extract the region of interest. The shape features of an image are extracted to provide distinguish properties between weed and crop. The classification of weed and crop has experimented with three different classifiers: Support Vector Machine, Artificial Neural Network and Convolutional Neural Network. The performance comparison of weed detection algorithms is executed on the Open CV and Keras platform using python language.