Aquatic weed automatic classification using machine learning techniques

Authors:
LuíS A. M. Pereira;Rodrigo Y. M. Nakamura;Guilherme F. S. De Souza;Dagoberto Martins;JoãO P. Papa
Affiliations:
UNESP - São Paulo State University, Department of Computing, Bauru, Brazil;UNESP - São Paulo State University, Department of Computing, Bauru, Brazil;UNESP - São Paulo State University, Department of Plant Production, Botucatu, Brazil;UNESP - São Paulo State University, Department of Plant Production, Botucatu, Brazil;UNESP - São Paulo State University, Department of Computing, Bauru, Brazil
Venue:
Computers and Electronics in Agriculture
Year:
2012

Citing 17
Cited 0

Optimal Correspondence of String Subsequences

IEEE Transactions on Pattern Analysis and Machine Intelligence
Support-Vector Networks

Machine Learning
BAS: a perceptual shape descriptor based on the beam angle statistics

Pattern Recognition Letters
The Image Foresting Transform: Theory, Algorithms, and Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing (3rd Edition)

Digital Image Processing (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)

Neural Networks: A Comprehensive Foundation (3rd Edition)
LIBLINEAR: A Library for Large Linear Classification

The Journal of Machine Learning Research
Supervised pattern classification based on optimum-path forest

International Journal of Imaging Systems and Technology - Contemporary Challenges in Combinatorial Image Analysis
Shape feature extraction and description based on tensor scale

Pattern Recognition
A Rotary-wing Unmanned Air Vehicle for Aquatic Weed Surveillance and Management

Journal of Intelligent and Robotic Systems
Application of support vector machine technology for weed and nitrogen stress detection in corn

Computers and Electronics in Agriculture
Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach
A computer vision approach for weeds identification through Support Vector Machines

Applied Soft Computing
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Efficient supervised optimum-path forest classification for large datasets

Pattern Recognition
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
An overview of statistical learning theory

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Aquatic weed control through chemical products has attracted much attention in the last years, mainly because of the ecological disorder caused by such plants, and also the consequences to the economical activities. However, this kind of control has been carried out in a non-automatic way by technicians, and may be a not healthy policy, since each species may react differently to the same herbicide. Thus, this work proposes the automatic identification of some species by means of supervised pattern recognition techniques and shape descriptors in order to compose a nearby future expert system for automatic application of the correct herbicide. Experiments using some state-of-the-art techniques have shown the robustness of the employed pattern recognition techniques.