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Govind Ballabh Pant University of Agriculture and Technology, Pantnagar

After independence, development of the rural sector was considered the primary concern of the Government of India. In 1949, with the appointment of the Radhakrishnan University Education Commission, imparting of agricultural education through the setting up of rural universities became the focal point. Later, in 1954 an Indo-American team led by Dr. K.R. Damle, the Vice-President of ICAR, was constituted that arrived at the idea of establishing a Rural University on the land-grant pattern of USA. As a consequence a contract between the Government of India, the Technical Cooperation Mission and some land-grant universities of USA, was signed to promote agricultural education in the country. The US universities included the universities of Tennessee, the Ohio State University, the Kansas State University, The University of Illinois, the Pennsylvania State University and the University of Missouri. The task of assisting Uttar Pradesh in establishing an agricultural university was assigned to the University of Illinois which signed a contract in 1959 to establish an agricultural University in the State. Dean, H.W. Hannah, of the University of Illinois prepared a blueprint for a Rural University to be set up at the Tarai State Farm in the district Nainital, UP. In the initial stage the University of Illinois also offered the services of its scientists and teachers. Thus, in 1960, the first agricultural university of India, UP Agricultural University, came into being by an Act of legislation, UP Act XI-V of 1958. The Act was later amended under UP Universities Re-enactment and Amendment Act 1972 and the University was rechristened as Govind Ballabh Pant University of Agriculture and Technology keeping in view the contributions of Pt. Govind Ballabh Pant, the then Chief Minister of UP. The University was dedicated to the Nation by the first Prime Minister of India Pt Jawaharlal Nehru on 17 November 1960. The G.B. Pant University is a symbol of successful partnership between India and the United States. The establishment of this university brought about a revolution in agricultural education, research and extension. It paved the way for setting up of 31 other agricultural universities in the country.

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Subject: Information Technology × Author: Gupta, Shally × Start date: 2017 × Type: Thesis ×
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    ThesisItemOpen Access
    Deep learning based approach for vehicle license plate recognition
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2020-11) Gupta, Shally; Singh, Rajesh S.
    In the field of Number Plate Recognition System Artificial Intelligence and Deep Learning have played a crucial role in recent years. Therefore this work aims to establish a model of plate detection with the aid of Deep Learning. It gives us an idea of the method of object detection which gives the utmost precision. The aim of this research is to extract accurate precise and optimize the number plate image recognition as compared to the previous research work done. It is therefore necessary to implement the ANPR system model based on an effective Deep Learning algorithms. An effective License Plate Recognition System is needed to ensure the successful functioning of intelligent transportation system. During the last few years, several methods of image processing such as OpenCV have been developed. These have not been exploited in the previous researches on number plate detection. Our study is based on Image Segmentation using object detection algorithm through YOLO (You Only Look Once) object detection technique in darkflow framework and character recognition based on Convolutional Neural Network (CNN). YOLO and CNN have proved to be the most productive techniques for several supervised and unsupervised learning tasks. In this research, we study deep learning algorithms and compare their performance. We have used custom dataset to for image segmentation and character recognition. Different categories of models for object detection is classified by performing segmentation using annotated images for detecting license plates by YOLO. Further, for recognizing characters, single character recognition using CNN is used and also trained a model as a base learners. Finally, compared the performance of the model by previous research for plate detection and have drawn useful conclusions. The result shows that Deep Learning algorithms outperform with high accuracy as compared to other image processing techniques.