S. Viswanadha Raju

Work place: Department of CSE JNTUHCEJ, Nachepally, Karimnagar

E-mail: svraju.jntu@gmail.com


Research Interests: Data Structures and Algorithms, Information Retrieval, Data Mining, Information Systems


Dr.S.Viswanadha Raju working as Professor of Computer Science and Engineering, has more than 20 years of experience in Teaching, Research and Administration. He obtained M.Tech from JNTUH and Ph.D. from Acharya Nagarjuna University Guntur. He certified CMI in Education Leadership and Program Management from Dudley college London which is sponsored by MHRD-AICTE and UKIERI. He has been honoured with Best computer Science engineering award from ISTE A.P., for the year 2014 and Best citizen of India etc. His research interests include Programming methodology;Algorithms, Information Retrieval, Biometrics, Databases, Data Mining,Research Methodology and Quality in Higher Education are globally recognized. He filed seven Indian patents deriving from his research.His research contributions are not only confined to his subject area but also extend to other related domains arising out of the new Education System, Assessment and Accreditation, and their impact on Indian Higher Education. To add impetus to his academic credentials he has undergone training as well as speaker for the quality improvement in education at NITTTR, WOSA-2012(world summit on accreditation 2012), WOSA-2014, TCS, Infosys, and NBA/NAAC. He produced 05Ph.Ds. and submitted 01Ph.D theses for evaluation. He is LifeMember of ISTE, IETE, CSI and IACSIT. He has 80 Research publications in reputed International/National Journals and Conferences. He has authored 02 book and implementing 02 AICTE sponsored research projects and one fellowship from TS&ST. He has been a member of Several Governmental Committees. He has served as Director MCA-GRIET, Head Dept of CSE, JNTUHCEJ. He delivered number of invited talks, expert lectures and key note addresses on various technical topics as well as Outcome Based Education, NBA/NAAC accreditation process, in the country and outside India.He has organized several workshops, seminars, tutorials and conferences. He has visited Singapore.

Author Articles
An Enhanced Feature Extraction Technique for Diagnosis of Pathological Problems in Mango Crop

By S. B. Ullagaddi S. Viswanadha Raju

DOI: https://doi.org/10.5815/ijigsp.2017.09.04, Pub. Date: 8 Sep. 2017

Lack of apparent shape and texture features in disease recognition (Powdery Mildew and Anthracnose) of crop is a key challenge of Agriculture domain in the last few decades. The various soft computing techniques exists in computer vision system still there is need of most efficient methods to meet accuracy. In this work An enhanced Wavelet-PCA based Statistical Feature Extraction technique along with Modified Rotation Kernel Transformation (MRKT) based directional features is proposed in order to address the issues arising in different methodologies for plant disease recognition. This enhanced scheme extracts twenty wavelet features in addition to twelve direction features for different plant parts mango flower, fruit and leaf. This research work is an extended part presents in reference 1 by the authors. The feature set of total 32 features is used to train with Artificial Neural Network to diagnose both Powdery Mildew and Anthracnose disease which occur in the form of Fungus and black spots respectively on different parts of mango plant. The results obtained are found with accuracy of 98.50%, 98.75%, and 98.70% respectively for flower, fruit and leaf 

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Automatic Robust Segmentation Scheme for Pathological Problems in Mango Crop

By S. B. Ullagaddi S. Viswanadha Raju

DOI: https://doi.org/10.5815/ijmecs.2017.01.05, Pub. Date: 8 Jan. 2017

Machine vision and soft computing techniques have been promising in the field of agriculture and horticulture to remove the barriers of conventional methods for detecting the plant diseases using different plant parts. Image segmentation technique is first and primary step in all the related researches such as fruit grading, leaf lesion region detection etc. In this paper, a robust technique for Mango crop using different plant parts such as Fruit, Flower and Leaf has been proposed in order to detect the disease more accurately. The captured real time images are pre-processed for illumination normalization and color space conversion before segmentation. The standard K-Means clustering scheme has been made adaptive and edge detection transforms have been applied to improve the segmentation results. Here, the objective function of K-Means clustering technique has been modified and cluster centers also have been updated to segment the diseased parts from images. The results obtained are better in the terms of both general human observation and in computational time.

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