S
Sudeep Marwaha
Researcher at Dept. of Computer Science, University of Delhi
Publications - 18
Citations - 206
Sudeep Marwaha is an academic researcher from Dept. of Computer Science, University of Delhi. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 1, co-authored 2 publications receiving 164 citations.
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Proceedings Article
Trust based recommender system for the semantic web
TL;DR: The presented recommender system uses temporal ontologies that absorb the effect of changes in the ontologies due to the dynamic nature of domains, in addition to the benefits of ontologies.
Journal ArticleDOI
Deep learning-based approach for identification of diseases of maize crop
Md. Ashraful Haque,Sudeep Marwaha,Chandan Kumar Deb,Sapna Nigam,Alka Arora,K. S. Hooda,P. Lakshmi Soujanya,S. K. Aggarwal,B. B. Lall,Mukesh Kumar,S. S. Islam,Mohit Panwar,Prabhat Kumar,Rakesh Agrawal +13 more
TL;DR: In this article , a deep learning approach for identification of in-field diseased images of maize crop has been proposed and three different architectures based on the framework of 'Inception-v3' network were trained with the collected diseased image of maize using baseline training approach.
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Deep transfer learning model for disease identification in wheat crop
Sapna Nigam,Rajni Jain,Sudeep Marwaha,Alka Arora,Md. Ashraful Haque,Akshay Dheeraj,Vaibhav Kumar Singh +6 more
TL;DR: In this article , an EfficientNet architecture-based model for wheat disease identification is proposed for automatically detecting major Wheat rusts, which can be easily integrated into mobile applications for use by stakeholders for image-based wheat disease detection in field conditions.
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SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques
TL;DR: This method presents an integrated deep learning platform of spikelet-based yield prediction comprising spike and spikelet detection, leading to higher precision over the existing methods.
Journal ArticleDOI
Leaf area assessment using image processing and support vector regression in rice
Tanuj Misra,Sudeep Marwaha,Alka Arora,Mrinmoy Ray,Shailendra Kumar,Sudhir Kumar,Viswanathan Chinnusamy +6 more
TL;DR: In this article , a non-destructive approach through digital image analysis has been presented to assess the total leaf area of rice plants grown in pot culture, images have been captured from four different angles with respect to the initial position of the camera.