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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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  • ThesisItemOpen Access
    Application of machine learning technique for diagnosis of powdery mildew disease in wheat
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-10) Negi, Archana; Nain, A.S.
    Powdery mildew is one of the most common fungal disease of wheat caused by an obligate biotrophic pathogen Blumeria graminis f. sp. tritici. Present investigation was conducted in rabi season 2020-21 at Norman E. Borlaug Crop Research Center, Pantnagar, Uttarakhand. The study was undertaken to create a disease diagnosis model for powdery mildew in wheat. Ten different deep learning approaches namely VGG16 (without augmentation), VGG16 (with under sampling), VGG16 (with over sampling), ResNet50 (without augmentation), ResNet50 (with under sampling), ResNet50 (with over sampling), ResNet50 (with under sampling and augmentation), EfficientNetB3 (with augmentation), EfficientNetB5 (with augmentation) and EfficientNetB7 (with augmentation) were used to check the best model for disease diagnosis. The accuracies attained by these algorithms were 61.7%, 59 %, 77 %, 58-63 %, 55-61 %, 74.7 %, 74%, 74.8 %, 73.6 % and 75.1 %, respectively. Automatic computer system for detecting and classifying of diseases is very important for efficient management. The present study will provide the opportunity for disease management by using advanced learning technologies with least interference of mankind. The study was also conducted to check the influence of weather parameters with disease progress of powdery mildew. Infection rate and PDI were used to analyze the effect of weather variables. PDI was positively correlated with both maximum (r=0.82) and minimum temperature (r=0.61) and positively for bright sunshine hours (r=0.81) while with morning (r=-0.73) and evening relative humidity (r=-0.77), it was negatively correlated. Maximum temperature (r=-0.52) and sunshine hours (r=-0.51) showed a negative correlation with the rate of infection while a positive correlation was seen with morning (r= 0.54) and evening relative humidity (r= 0.61). Step-wise multiple regression analysis was done and a prediction equation was developed (R2=0.45).