Improving weed pressure assessment using digital images from an experience-based reasoning approach

Authors:
Xavier P. Burgos-Artizzu;Angela Ribeiro;Alberto Tellaeche;Gonzalo Pajares;Cesar Fernández-Quintanilla
Affiliations:
Artificial Perception Group (GPA), Instituto de Automática Industrial (IAI), Spanish National Research Council (CSIC), Arganda del Rey, Madrid, Spain;Artificial Perception Group (GPA), Instituto de Automática Industrial (IAI), Spanish National Research Council (CSIC), Arganda del Rey, Madrid, Spain;Dpto de Informática y Automática, E.T.S. Informática, Universidad Nacional de Educación a Distancia (UNED), Spain;Dpto. de Ingeniería del Software e Inteligencia Artificial, Facultad de Informática, Universidad Complutense de Madrid (UCM), Madrid, Spain;Centro de Ciencias Medioambientales (CCMA), Spanish National Research Council (CSIC), Madrid, Spain
Venue:
Computers and Electronics in Agriculture
Year:
2009

Citing 8
Cited 8

Case-based reasoning: foundational issues, methodological variations, and system approaches

AI Communications
Distance-based functions for image comparison

Pattern Recognition Letters
Case-Based Reasoning: Experiences, Lessons and Future Directions

Case-Based Reasoning: Experiences, Lessons and Future Directions
Digital Image Processing

Digital Image Processing
An Agricultural Mobile Robot with Vision-Based Perception for Mechanical Weed Control

Autonomous Robots
A Two Layer Case-Based Reasoning Architecture for Medical Image Understanding

EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Image indexing and similarity retrieval based on spatial relationship model

Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Introduction to multimedia and mobile agents
Large-scale investigation of weed seed identification by machine vision

Computers and Electronics in Agriculture

Pure reactive behavior learning using Case Based Reasoning for a vision based 4-legged robot

Robotics and Autonomous Systems
Original paper: Automatic segmentation of relevant textures in agricultural images

Computers and Electronics in Agriculture
Towards machine vision based site-specific weed management in cereals

Computers and Electronics in Agriculture
Support Vector Machines for crop/weeds identification in maize fields

Expert Systems with Applications: An International Journal
Automatic detection of crop rows in maize fields with high weeds pressure

Expert Systems with Applications: An International Journal
Automatic expert system for weeds/crops identification in images from maize fields

Expert Systems with Applications: An International Journal
Automatic expert system based on images for accuracy crop row detection in maize fields

Expert Systems with Applications: An International Journal
A new Expert System for greenness identification in agricultural images

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

One of the main goals of Precision Agriculture is site-specific crop management to reduce the production of herbicide residues. This paper presents a computer-based image analysis system allowing users to input digital images of a crop field, and to process these by a series of methods to enable the percentages of weeds, crop and soil present in the image to be estimated. The system includes a Case-Based Reasoning (CBR) system that, automatically and in real time, determines which processing method is the best for each image. The main challenge in terms of image analysis is achieving appropriate discrimination between weeds, crop and soil in outdoor field images under varying light, soil background texture and crop damage conditions. The performance of the developed system is shown for a set of images acquired from different fields and under different, uncontrolled conditions, such as different light, crop growth stage and size of weeds, reaching correlation coefficients with real data of almost 80%.