Segmentation and description of natural outdoor scenes
Image and Vision Computing
Spatial and spectral methods for weed detection and localization
EURASIP Journal on Applied Signal Processing
Original paper: A vision based row detection system for sugar beet
Computers and Electronics in Agriculture
Crop/weed discrimination in perspective agronomic images
Computers and Electronics in Agriculture
A new vision-based approach to differential spraying in precision agriculture
Computers and Electronics in Agriculture
Autonomous robotic weed control systems: A review
Computers and Electronics in Agriculture
Verification of color vegetation indices for automated crop imaging applications
Computers and Electronics in Agriculture
Mean-shift-based color segmentation of images containing green vegetation
Computers and Electronics in Agriculture
Analysis of natural images processing for the extraction of agricultural elements
Image and Vision Computing
Large-scale investigation of weed seed identification by machine vision
Computers and Electronics in Agriculture
Line cluster detection using a variant of the Hough transform for culture row localisation
Image and Vision Computing
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
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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This paper presents a computer vision system that successfully discriminates between weed patches and crop rows under uncontrolled lighting in real-time. The system consists of two independent subsystems, a fast image processing delivering results in real-time (Fast Image Processing, FIP), and a slower and more accurate processing (Robust Crop Row Detection, RCRD) that is used to correct the first subsystem's mistakes. This combination produces a system that achieves very good results under a wide variety of conditions. Tested on several maize videos taken of different fields and during different years, the system successfully detects an average of 95% of weeds and 80% of crops under different illumination, soil humidity and weed/crop growth conditions. Moreover, the system has been shown to produce acceptable results even under very difficult conditions, such as in the presence of dramatic sowing errors or abrupt camera movements. The computer vision system has been developed for integration into a treatment system because the ideal setup for any weed sprayer system would include a tool that could provide information on the weeds and crops present at each point in real-time, while the tractor mounting the spraying bar is moving.