Suchit Purohit

Work place: Department of Computer Science Gujarat University Ahmedabad, India



Research Interests: Image Processing, Image Manipulation, Image Compression, Pattern Recognition, Computer systems and computational processes


Mrs. Suchit S. Purohit is currently working as Asst. Professor in Department of Computer Science, Gujarat University, Ahmedabad, India. She earned her master’s degree from M.B.M. Engineering College Jodhpur, Rajasthan. She is pursuing her Ph.D. from Department Of Computer Science, Gujarat University. The area of research is object recognition and Image processing applied to planetary images. She is a member of IEEE Geoscience and Remote sensing Society and Indian Society of Geomatics. She is currently is CoPrincipal Investigator research projects funded by ISRO/DOS, India. She is coordinating elearning content development under project funded by MHRD, India. She has many publications in national and International peer reviewed journals. She is serving as a reviewer in many international journals and member of TPC in International conferences.

Author Articles
Application of Sparse Coded SIFT Features for Classification of Plant Images

By Suchit Purohit Savita R. Gandhi

DOI:, Pub. Date: 8 Oct. 2017

Automated system for plant species recognition is need of today since manual taxonomy is cumbersome, tedious, time consuming, expensive and suffers from perceptual biasness as well as taxonomic impediment. Availability of digitized databases with high resolution plant images annotated with metadata like date and time, lat long information has increased the interest in development of automated systems for plant taxonomy. Most of the approaches work only on a particular organ of the plant like leaf, bark or flowers and utilize only contextual information stored in the image which is time dependent whereas other metadata associated should also be considered. Motivated from the need of automation of plant species recognition and availability of digital databases of plants, we propose an image based identification of species of plant when the image may belong to different plant parts such as leaf, stem or flower, fruit , scanned leaf, branch and the entire plant. Besides using image content, our system also uses metadata associated with images like latitude, longitude and date of capturing to ease the identification process and obtain more accurate results. For a given image of plant and associated metadata, the system recognizes the species of the given plant image and produces an output that contains the Family, Genus, and Species name. Different methods for recognition of the species are used according to the part of the plant to which the image belongs to. For flower category, fusion of shape, color and texture features are used. For other categories like stem, fruit, leaf and leafscan, sparsely coded SIFT features pooled with Spatial pyramid matching approach is used. The proposed framework is implemented and tested on ImageClef data with 50 different classes of species. Maximum accuracy of 98% is attained in leaf scan sub-category whereas minimum accuracy is achieved in fruit sub-category which is 67.3 %.

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