Jyoti Saini et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 105-109
[10]. Fruit image segmentation is one of the growing research area in agricultural image processing. A work on fruit segmentation using multi-spectral feature analysis was presented by Calvin Hung. Author defined the conditional analysis along with pervised feature learning. Author defined the variance analysis using caopy tree to improve robustness and accuracy [11]. III. ASPECTS OF AGRICULTRE IMAGES PROCESSING APPROACHES To perform the agriculture image processing, the foremost task is the extraction of the image features. Image segmentation is about to extract the image features. To perform this feature extraction, the image analysis is been done under different aspects. These aspects include the change detection, intensity analysis, color intensity analysis etc. In case of agricultural processing such as leaf recognition or the fruit identification, the shape and size of leaf and fruits is performed. To perform this kind of recognition, the corner detection is performed. These corner points are used to determine the contour characteristics identification from the image. To perform the effective feature identification, the feature extraction and recognition approaches are divided in different vectors. These classes are shown in figure 4. Agriculture Image Feature Detection
Region Feature Extraction
Grain Feature Extraction
Point Feature Extraction Feature Matching
Area Based Matching
Feature Based Matching
Transform Model Estimation
Image Resampling
Figure 4: Agricultural Image Segmentation Process A)
Agricultural Image Feature Extraction
Feature extraction is about to identify the valuable information from the image based on which the decision regarding the image processing can be performed. This feature extraction can be performed in terms of extraction of image shape, intersection points, edge extraction etc. This feature extraction process includes the identification of grain ending points, distinctive point extractive, center of gravity point extraction etc. The feature extraction approaches are: i) Region Features Region features are defined as the closed boundary features that are defined with appropriate size specification such as leaf shape, grain shape etc. The regions are also defined respective to view point, rotation angle etc. The accurate area identification is the tune up process under the segmentation parameters. ii) Grain Features Grain feature are used to extract the contour feature extraction, identification of disease impact, pest detection etc. Edge detection analysis is also used to perform the structure detection of the leaf or the fruit. iii) Point Feature Point feature is the lowest level of feature extraction. It includes the extraction of intersection point extraction, centralized point extraction, corner point identification. The curvature analysis along with discontinuities detection comes under this kind of detection.
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