The nature of statistical learning theory
The nature of statistical learning theory
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
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
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
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
| Hi-index | 12.06 |
In Precision Agriculture (PA) automatic image segmentation for plant identification is an important issue to be addressed. Emerging technologies in optical imaging sensors play an important role in PA. In maize fields, site-specific treatments, with chemical products or mechanical manipulations, are applied for weeds elimination. Maize is an irrigated crop, also unprotected from rainfall. After a strong rain, soil materials (particularly clays) mixed with water impregnate the vegetative cover. The green spectral component associated to the plants is masked by the dominant red spectral component coming from soil materials. This makes methods based on the greenness identification fail under such situations. We propose a new method based on Support Vector Machines for identifying plants with green spectral components masked and unmasked. The method is also valid for post-treatment evaluation, where loss of greenness in weeds is identified with the effectiveness of the treatment and in crops with damage or masking. The performance of the method allows to verify its viability for automatic tasks in agriculture based on image processing.