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
Verification of color vegetation indices for automated crop imaging applications
Computers and Electronics in Agriculture
An expert system based on fuzzy entropy for automatic threshold selection in image processing
Expert Systems with Applications: An International Journal
Expert system for pests, diseases and weeds identification in olive crops
Expert Systems with Applications: An International Journal
Mean-shift-based color segmentation of images containing green vegetation
Computers and Electronics in Agriculture
Improving weed pressure assessment using digital images from an experience-based reasoning approach
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Pattern Recognition Letters
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
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
Hi-index | 12.05 |
Automation for the identification of plants, based on imaging sensors, in agricultural crops represents an important challenge. In maize fields, site-specific treatments, with chemical products or mechanical manipulations, can be applied for weeds elimination. This requires the identification of weeds and crop plants. Sometimes these plants appear impregnated by materials coming from the soil (particularly clays). This appears when the field is irrigated or after rain, particularly when the water falls with some force. This makes traditional approaches based on images greenness identification fail under such situations. Indeed, most pixels belonging to plants, but impregnated, are misidentified as soil pixels because they have lost their natural greenness. This loss of greenness also occurs after treatment when weeds have begun the process of death. To correctly identify all plants, independently of the loss of greenness, we design an automatic expert system based on image segmentation procedures. The performance of this method is verified favorably.