Computer Vision for Weed Identification in Corn Plants Using Modified Support Vector Machine

Abstract

Weed plants are unwanted plants growing in between host plants. There are more than 8000 weed species in the agriculture field. This is the global issue that leads to loss in both the quality and quantity of the product. So, attention has to be taken to avoid these losses and save manpower. In this chapter, the three procedures, segmentation, feature extraction, and classification, for weed plant identification are presented in detail. To separate the region of interest, threshold segmentation method was applied. Then the important features, shape, and textures were analysed with the help of GLCM method, which are discussed in this review. Finally, in the image classification method, modified support vector machine was used to separate the weed and host plants. Finally, this modified SVM was compared with CNN using performance analyses and produced high accuracy of 98.56% compared to existing systems. Hence, the farmers are expected to adopt these technologies to overcome the agricultural problems.