Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
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
Performance analysis of homomorphic systems for image change detection
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Support Vector Machines for crop/weeds identification in maize fields
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
Crop segmentation from images by morphology modeling in the CIE L*a*b* color space
Computers and Electronics in Agriculture
Hi-index | 0.01 |
One important issue emerging strongly in agriculture is related with the automatization of tasks, where the optical sensors play an important role. They provide images that must be conveniently processed. The most relevant image processing procedures require the identification of green plants, in our experiments they come from barley and corn crops including weeds, so that some types of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations. Also the identification of textures belonging to the soil could be useful to know some variables, such as humidity, smoothness or any others. Finally, from the point of view of the autonomous robot navigation, where the robot is equipped with the imaging system, some times it is convenient to know not only the soil information and the plants growing in the soil but also additional information supplied by global references based on specific areas. This implies that the images to be processed contain textures of three main types to be identified: green plants, soil and sky if any. This paper proposes a new automatic approach for segmenting these main textures and also to refine the identification of sub-textures inside the main ones. Concerning the green identification, we propose a new approach that exploits the performance of existing strategies by combining them. The combination takes into account the relevance of the information provided by each strategy based on the intensity variability. This makes an important contribution. The combination of thresholding approaches, for segmenting the soil and the sky, makes the second contribution; finally the adjusting of the supervised fuzzy clustering approach for identifying sub-textures automatically, makes the third finding. The performance of the method allows to verify its viability for automatic tasks in agriculture based on image processing.