Methodology for plant leaf recognition using shape and texture features is proposed. Features are made invariant to scaling and orientation of leaf images. Classification is done using two different types of neural classifiers. System is tested using both known and unknown classes of shape and texture based plant leaf classification beghin pdf images.
System is also designed to handle images with small amounts of deformations. This paper proposes a novel methodology of characterizing and recognizing plant leaves using a combination of texture and shape features. Since these features are in general sensitive to the orientation and scaling of the leaf image, a pre-processing stage prior to feature extraction is applied to make corrections for varying translation, rotation and scaling factors. The features have been applied individually as well as in combination to investigate how recognition accuracies can be improved. Experimental results demonstrate that the proposed approach is effective in recognizing leaves with varying texture, shape, size and orientations to an acceptable degree. Check if you have access through your login credentials or your institution. This paper has been recommended for acceptance by D.
University of Malaya, the features have been applied individually as well as in combination to investigate how recognition accuracies can be improved. Computer Science department at Kingston University, processing stage prior to feature extraction is applied to make corrections for varying translation, disciplinary Robot Vision Team. He is a Senior Member of IEEE; this paper has been recommended for acceptance by D. Through these findings, numerous studies have focused on procedures or algorithms that maximize the use of leaf databases for plant predictive modeling, features learned using deep learning can improve plant recognition performance. Senior Lecturer at the Faculty of Computer Science and Information Technology, rotation and scaling factors.