Application of rectangular features for the localization of fertile material in plant images

Analysis of fertile material such as flowers and fruit is a key factor in the proper identification of plant species. Despite object recognition being a mature research area, the use of it in automated plant identification is still relatively new. This paper describes a novel method of detecting fertile material in plant images using rectangular features. Rectangular features are obtained for the grayscale image, the J value image, the magnitude and angle of the gradient of the image. From these, the features with the best performance are selected based on their ability to detect fertile material (flowers) and non-fertile material (leaves). Based on performance, the rectangular features of the grayscale image and J value image are used. Multiple Support Vector Machine (SVM) classifiers with different kernels are tested and the final result is obtained using classifier voting based on the confidence of each classifier. After applying the classifier to the entire image, regions of interest of fertile material are isolated and post processed.