Weed Detection Approach Using Feature Extraction and KNN Classification

Abstract

The image processing is the most important technology involved in the agricultural fields. One of the basic objectives of agricultural production is to gain maximum crop yield at low cost. The yield and subsequent profit can be enhanced by detecting and managing the issues related to crop yield indicators in early stage. The crop yield indicators such as weed can be detected and removed by different manual and automatic techniques. Image processing is the most popular technique to detect weed in the field crop. The technique of textural feature analysis and morphological scanning is applied to sugar beet plant in this paper. At last, KNN classifier is applied which can classify weed plant from field crop. The results of the weed detection are analyzed in terms of accuracy and execution time.