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.

Browse

Search Results

Now showing 1 - 9 of 12

Open Access
Spatio-temporal analysis of vegetation dynamics of New Delhi (India) using satellite data
(G.B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand. PIN - 263145, 2022-10) Navtej Anand; Subodh Prasad
Understanding and analysing vegetation cover changes is crucial for a number of reasons, especially when it comes to taking the necessary conservation measures. This study asses the vegetation changes in the New Delhi (India) over the years from 2000 to march 2022 based on NDVI (Normalized Difference Vegetation Index). The NDVI values have been collected from MODIS terra satellite imagery. Using this NDVI data the study finds that the vegetation greenness of Delhi has increased by 18.63% from year 2001 to 2021. A dataset of 509 NDVI values have been used for making the time series. An attempt has been carried out to predict the vegetation change using this MODIS NDVI time series data and LSTM (Long Short Term Memory) network. The prediction has been carried out on two different LSTM models side by side on the same data and comparative study has been done. The LSTM networks has been trained with 80% of the data and rest 20% are used for testing the model’s accuracy. The results show that both the LSTM model are capable of predicting the future NDVI values with appreciable accuracy but model-1 predicts with better accuracy and lesser errors. Model-1 predicts the future NDVI values with RMSE less than 0.034 and R2 of more than 0.77. Model-2 is not far behind, it predicts with RMSE of around 0.036 and R2 of around 0.74. So, this study concludes that using LSTM networks it is possible to accurately predict vegetation changes well in advance and take appropriate proactive measures to protect and enhance the vegetation in any area.
Open Access
Air quality assessment of Uttarakhand (India) based on satellite data
(G.B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand. PIN - 263145, 2022-10) Chandra, Divyanshu; Verma, Govind
Degrading Air Quality is a major concern for all species on this planet. Over the years, it is seen that air quality is constantly degrading mainly of the reasons of industrialisation, deforestation, and green house effect. Main parameters to be considered with the Air Quality are the Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3) and Aerosols. They are present in the air and their increasing or decreasing nature causes major changes in the air that organism’s breath. A study of these parameters changing over time is necessary so to keep a check on the degrading air quality. In this study, the data of Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3) and Aerosols is taken for the past 5 years and their time series is extracted thereafter a test on stationarity is done so as to know whether these series are stationary or not. Two machine learning models namely Holt winter’s Smoothing and FbProphet is applied to predict the value adjacent to the original value and a error metric is comparison is done to find out which model is best suited for forecasting these Air Quality parameters.
Open Access
Kidney stone detection from ultrasound images using masking techniques
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-12) Chaudhary, Harshita; Pandey, Binay Kumar
Here we are using masking techniques for stone detection that are present in the kidney. As we know Masking techniques are conspicuous approaches in contrast enhancement. For this firstly, the image is converted into grey and after that contrast of the image is enhanced. The process of contrast enhancement is done with the help of Optimum Wavelet-Based Masking (OWBM) using the Enhanced Cuckoo Search Algorith (ECSA). Afterward image segmentation and image masking have been done to detect stone from the image. The cuckoo search algorithm is used for global optimization of contrast enhancement. With the help of the Cuckoo search algorithm approximation of the coefficient has been optimized. The objective of this project is to design and implement a method to detect the presence of stone from the ultrasound image of a kidney. Here we are making are system our more intelligent.
Open Access
IoT based surveillance system using DNN
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-12) Rawat, Rahul; Srivastava, Ratnesh Prasad
Localization, Visibility, Proximity, Detection, Recognition has always been a challenge for surveillance system. These challenges can be felt in the industries where surveillance systems are used like armed forces, technical-agriculture and other such fields. A way to get the ease of mind would be installing a security camera. Most of the smart system available are just for the surveillance of human intervention but there is a need for a system which can be used for animals as well because with the outburst of human population and symbiotic relationship with wild animals results in life loss and damage to agriculture. There are many electrical equipment’s available for home which can do the monitoring from a remote area all at a time. In this paper we are designing to overcome these above-mentioned challenges for human and animal-based surveillance system in real time application. The system setup is done on a Raspberry pi integrated with deep-learning models which performs the classification of objects on the frames, then the classified objects is given to a face detection model for further processing. The detected face is relayed to the back-end for feature mapping with the saved log files with containing features of familiar face IDs. Four models were tested for face detection out of which the DNN model performed the best giving an accuracy of 87.88%. The system is also able to send alerts to the admin if any threat is detected with the help of a communication module.
Open Access
Detection of Ransomware using machine learning
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-02) Pujari, Shivam Kumar; Mandoria, H.L.
Today’s world depends on the cyber world, since it is very useful for collecting information, data and transporting them. Since anyone can use it, for security purpose of data a technique called encryption is made. Unfortunately, this strong technique of encryption for security is also useful for hackers to lock any file or system by encryption by infecting malware. This type of malware which encrypt data is called Ransomware. In the digital world there are various types of attacks for a different aspect of motive such as economic benefits, personal issues, religious issues, political benefits, or special propaganda, etc. Ransomware attacks are for financial benefits and most popular in today's world. We purpose a method in which we can classify and detect ransomware and some other malware also
Open Access
A comparative study of edge detection techniques on different images using Scilab
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-01) Rajshree Kumari; Ashok Kumar
In the field of image processing, edge detection plays a significant role in recent years to detect images more accurately, for this purpose it is important to choose the edge detection techniques wisely and correctly based on their properties. Therefore this work aims to give a comparative study of different edge detection technique using some parameters to check which techniques gives more accurate result with some parameters. The aim of this research is to study different edge detection to detect edges under different circumstances. In this research we have proposed a comparative study between the three-edge detection techniques Canny, Sobel, and Prewitt, using different types of images under different parameters for analysis. The software tool that we have used here is Scilab which is an open source tool and an alternative to MATLAB. This study is tested by conducting experiments on the WANG image dataset and benchmark standard images. The results of this comparative studyz suggest that Prewitt works better than other edge detections with greater accuracy under certain parameters and in different image types.
Open 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.
Open Access
A novel approach to improve the Z-SEP protocol in WSNs
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-08) Dhek, Ankita; Mandoria, H.L.
The eminence of Wireless Sensor Networks (WSNs) has grown massively in the past years because WSN has the possibility to associate the physical world with the virtual world by framing a system of various sensor nodes by deploying it in a network. Here, sensor nodes are typically battery-driven gadgets, and consequently energy consumption of sensor nodes is a noteworthy design issue. To handle this issue Cluster based routing is most popular routing technique in Wireless Sensor Networks(WSNs). Due to various need of WSN applications, efficient energy utilization in routing protocols is still a potential area of research. The proposed work tried to overcome the problem of energy contraint in Z-SEP protocol in such a way that energy consumption of every node in the wireless sensor networks is minimized with the help of improving the stability period, instability period ,network lifetime and throughput. This thesis solves the problem by proposing a clustering based single-sink routing protocol for heterogeneous wireless sensor networks in which 3 types of nodes are introduced. The idea of multi-hop communication and distance factor is considered.Using the MATLAB simulation the result is compared with the Z-SEP protocol and the result shows that the proposed protocol outperforms the Z-SEP protocol.
Open Access
Intrusion detection using ensemble methods and deep learning
(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-08) Rai, Mahima; Mandoria, H.L.
In recent years Machine Learning and Deep Learning have played a crucial role in the field of cyber security. This research therefore intends to design IDS with a good mix of various ML and DL classifiers. It gives us an idea about which classifier gives the highest accuracy. The aim of this research is to classify whether the packet is a normal data packet or attacked data packet. Therefore the model of IDS should be implemented based on effective and significant ML and DL algorithms. To ensure network security, an effective intrusion detection system is required. Several ensemble methods like XG-Boost and LGBM have been developed in the past 4-5 years. These have not been exploited in the previous researches on anomaly detection. Our study will make use of these novel Gradient Boosting Decision Tree algorithms. XG-Boost and LGBM have proved to be the most productive techniques for several supervised and unsupervised learning tasks. In this research, we study several machine learning and deep learning classifiers and compare their performance. We have used the NSL KDD dataset to predict the probability of occurrence of 21 different classes of attacks on a network and three different categories of models - Linear Models including Logistic Regression and Stochastic Gradient Descent (SGD) classifier; Gradient Boosting Decision Tree ensembles including LightGBM (LGBM) and XG-Boost; and a Deep Neural Network (DNN) classifier and also trained a stacked model consisting of all these models as base learners. Finally, compared the performances of all the models for Network Intrusion Detection using the NSL-KDD dataset and have drawn useful conclusions. The simulation result shows that Light GBM, XGBoost, and stacked classifiers outperform with high accuracy as compared to Logistic regression, Stochastic Gradient Descent Classifier and Deep Neural network. All of these are predictive analysis techniques.

Govind Ballabh Pant University of Agriculture and Technology, Pantnagar

Browse

Thesis Type

Type

Author

Date

Subject

Search Results