Extensions of the TOPSIS for group decision-making under fuzzy environment
Fuzzy Sets and Systems
Evaluation of global image thresholding for change detection
Pattern Recognition Letters
Large-scale investigation of weed seed identification by machine vision
Computers and Electronics in Agriculture
Using fuzzy data mining to evaluate survey data from olive grove cultivation
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Analysis of natural images processing for the extraction of agricultural elements
Image and Vision Computing
Computers and Electronics in Agriculture
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
Towards machine vision based site-specific weed management in cereals
Computers and Electronics in Agriculture
Computer vision for fruit harvesting robots state of the art and challenges ahead
International Journal of Computational Vision and Robotics
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
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One of the objectives of precision agriculture is to minimize the volume of herbicides by using site-specific weed management systems. To reach this goal, two major factors need to be considered: (1) the similarity of spectral signatures, shapes, and textures between weeds and crops and (2) irregular distribution of weeds within the crop. This paper outlines an automatic computer vision method for detecting Avena sterilis, a noxious weed growing in cereal crops, and differential spraying to control the weed. The proposed method determines the quantity and distribution of weeds in the crop fields and applies a decision-making strategy for selective spraying, which forms the main focus of the paper. The method consists of two stages: image segmentation and decision-making. The image segmentation process extracts cells from the image as the low-level units. The quantity and distribution of weeds in the cell are mapped as area and structural based attributes, respectively. From these attributes, a multicriteria decision-making approach under a fuzzy context allows us to decide whether any given cell needs to be sprayed. The method was compared with other existing strategies.