A computer vision approach for weeds identification through Support Vector Machines

Authors:
Alberto Tellaeche;Gonzalo Pajares;Xavier P. Burgos-Artizzu;Angela Ribeiro
Affiliations:
Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense, Madrid 28040, Spain;Dpto. Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense, Madrid 28040, Spain;Instituto de Automática Industrial, CSIC, Arganda del Rey, Madrid, Spain;Instituto de Automática Industrial, CSIC, Arganda del Rey, Madrid, Spain
Venue:
Applied Soft Computing
Year:
2011

Citing 10
Cited 4

The nature of statistical learning theory

The nature of statistical learning theory
Statistical Learning Theory: A Primer

International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Learning from Data: Concepts, Theory, and Methods

Learning from Data: Concepts, Theory, and Methods
An Agricultural Mobile Robot with Vision-Based Perception for Mechanical Weed Control

Autonomous Robots
Evaluation of global image thresholding for change detection

Pattern Recognition Letters
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Digital Image Processing Using MATLAB

Digital Image Processing Using MATLAB
A vision-based method for weeds identification through the Bayesian decision theory

Pattern Recognition
A new vision-based approach to differential spraying in precision agriculture

Computers and Electronics in Agriculture
Large-scale investigation of weed seed identification by machine vision

Computers and Electronics in Agriculture

Sequential support vector machine classification for small-grain weed species discrimination with special regard to Cirsium arvense and Galium aparine

Computers and Electronics in Agriculture
Automatic detection of crop rows in maize fields with high weeds pressure

Expert Systems with Applications: An International Journal
Aquatic weed automatic classification using machine learning techniques

Computers and Electronics in Agriculture
Automatic expert system based on images for accuracy crop row detection in maize fields

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper outlines an automatic computer vision system for the identification of avena sterilis which is a special weed seed growing in cereal crops. The final goal is to reduce the quantity of herbicide to be sprayed as an important and necessary step for precision agriculture. So, only areas where the presence of weeds is important should be sprayed. The main problems for the identification of this kind of weed are its similar spectral signature with respect the crops and also its irregular distribution in the field. It has been designed a new strategy involving two processes: image segmentation and decision making. The image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and weeds. The decision making is based on the Support Vector Machines and determines if a cell must be sprayed. The main findings of this paper are reflected in the combination of the segmentation and the Support Vector Machines decision processes. Another important contribution of this approach is the minimum requirements of the system in terms of memory and computation power if compared with other previous works. The performance of the method is illustrated by comparative analysis against some existing strategies.