Bayes Error Estimation Using Parzen and k-NN Procedures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-spectral vision system for weed detection
Pattern Recognition Letters
Digital Image Processing
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
On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions
IEEE Transactions on Computers
Large-scale investigation of weed seed identification by machine vision
Computers and Electronics in Agriculture
Mean-shift-based color segmentation of images containing green vegetation
Computers and Electronics in Agriculture
The Analytical Hierarchy Process for contaminated land management
Advanced Engineering Informatics
Pattern Recognition Letters
A computer vision approach for weeds identification through Support Vector Machines
Applied Soft Computing
Original paper: Automatic segmentation of relevant textures in agricultural images
Computers and Electronics in Agriculture
Original paper: Real-time image processing for crop/weed discrimination in maize fields
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Recognition of porosity in wood microscopic anatomical images
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
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
Computers and Electronics in Agriculture
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One of the objectives of precision agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. This paper outlines an automatic computer vision-based approach for the detection and differential spraying of weeds in corn crops. The method is designed for post-emergence herbicide applications where weeds and corn plants display similar spectral signatures and the weeds appear irregularly distributed within the crop's field. The proposed strategy involves two processes: image segmentation and decision making. 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 measuring relationships between crop and weeds. The decision making determines the cells to be sprayed based on the computation of a posterior probability under a Bayesian framework. The a priori probability in this framework is computed taking into account the dynamic of the physical system (tractor) where the method is embedded. The main contributions of this paper are: (1) the combination of the image segmentation and decision making processes and (2) the decision making itself which exploits a previous knowledge which is mapped as the a priori probability. The performance of the method is illustrated by comparative analysis against some existing strategies.