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Detection of Weed Using Visual Attention Model and SVM Classifier

  • Manda Aparna
  • D. RadhaEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Agriculture is one of the provenances of human ailment in this heavenly body. It plays an extrusive role in the economy. Flourishing crops are a constituent of agriculture. Weeds are the additional plants to the crop. Removal of weeds is a challenging job for the farmers as it is a periodic, time–consuming, and cost-intensive process. Different ways to remove those weeds are by hand labor, spraying pesticides and herbicides, and machines but with their own disadvantages. The software solution can overcome these drawbacks to an extent. The main concern in software is in the identification of weeds among the crops in the field. The proposed system helps in detection of weeds in the agriculture field using computer vision methods. The method works with a dataset of crops and weeds. The plants are identified as salient regions in visual attention model and the identified plants are classified as crops or weeds using support vector machine classifier.

Keywords

Weeds Computer vision method Visual attention model Support vector machine classifier 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringAmrita School of Engineering, Amrita Vishwa VidyapeethamBengaluruIndia

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