Pandit Deendayal Petroleum University

About: Pandit Deendayal Petroleum University is a education organization based out in Gandhinagar, Gujarat, India. It is known for research contribution in the topics: Welding & Perovskite (structure). The organization has 996 authors who have published 1804 publications receiving 16594 citations.

A comprehensive review on automation in agriculture using artificial intelligence

Abstract: Agriculture automation is the main concern and emerging subject for every country. The world population is increasing at a very fast rate and with increase in population the need for food increases briskly. Traditional methods used by farmers aren't sufficient enough to serve the increasing demand and so they have to hamper the soil by using harmful pesticides in an intensified manner. This affects the agricultural practice a lot and in the end the land remains barren with no fertility. This paper talks about different automation practices like IOT, Wireless Communications, Machine learning and Artificial Intelligence, Deep learning. There are some areas which are causing the problems to agriculture field like crop diseases, lack of storage management, pesticide control, weed management, lack of irrigation and water management and all this problems can be solved by above mentioned different techniques. Today, there is an urgent need to decipher the issues like use of harmful pesticides, controlled irrigation, control on pollution and effects of environment in agricultural practice. Automation of farming practices has proved to increase the gain from the soil and also has strengthened the soil fertility. This paper surveys the work of many researchers to get a brief overview about the current implementation of automation in agriculture. The paper also discusses a proposed system which can be implemented in botanical farm for flower and leaf identification and watering using IOT.

Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides

Abstract: Agriculture plays a significant role in the economic sector. The automation in agriculture is the main concern and the emerging subject across the world. The population is increasing tremendously and with this increase the demand of food and employment is also increasing. The traditional methods which were used by the farmers, were not sufficient enough to fulfill these requirements. Thus, new automated methods were introduced. These new methods satisfied the food requirements and also provided employment opportunities to billions of people. Artificial Intelligence in agriculture has brought an agriculture revolution. This technology has protected the crop yield from various factors like the climate changes, population growth, employment issues and the food security problems. This main concern of this paper is to audit the various applications of Artificial intelligence in agriculture such as for irrigation, weeding, spraying with the help of sensors and other means embedded in robots and drones. These technologies saves the excess use of water, pesticides, herbicides, maintains the fertility of the soil, also helps in the efficient use of man power and elevate the productivity and improve the quality. This paper surveys the work of many researchers to get a brief overview about the current implementation of automation in agriculture, the weeding systems through the robots and drones. The various soil water sensing methods are discussed along with two automated weeding techniques. The implementation of drones is discussed, the various methods used by drones for spraying and crop-monitoring is also discussed in this paper.

Arsenic removal by electrocoagulation process: Recent trends and removal mechanism

Abstract: Arsenic contamination in drinking water is a major issue in the present world. Arsenicosis is the disease caused by the regular consumption of arsenic contaminated water, even at a lesser contaminated level. The number of arsenicosis patients is increasing day-by-day. Decontamination of arsenic from the water medium is the only one way to regulate this and the arsenic removal can be fulfilled by water treatment methods based on separation techniques. Electrocoagulation (EC) process is a promising technology for the effective removal of arsenic from aqueous solution. The present review article analyzes the performance of the EC process for arsenic removal. Electrocoagulation using various sacrificial metal anodes such as aluminium, iron, magnesium, etc. is found to be very effective for arsenic decontamination. The performances of each anode are described in detail. A special focus has been made on the mechanism behind the arsenite and arsenate removal by EC process. Main trends in the disposal methods of sludge containing arsenic are also included. Comparison of arsenic decontamination efficiencies of chemical coagulation and EC is also reported.

A Comparative Analysis of Logistic Regression, Random Forest and KNN Models for the Text Classification

Abstract: In the current generation, a huge amount of textual documents are generated and there is an urgent need to organize them in a proper structure so that classification can be performed and categories can be properly defined. The key technology for gaining the insights into a text information and organizing that information is known as text classification. The classes are then classified by determining the text types of the content. Based on different machine learning algorithms used in the current paper, the system of text classification is divided into four sections namely text pre-treatment, text representation, implementation of the classifier and classification. In this paper, a BBC news text classification system is designed. In the classifier implementation section, the authors separately chose and compared logistic regression, random forest and K-nearest neighbour as our classification algorithms. Then, these classifiers were tested, analysed and compared with each other and finally got a conclusion. The experimental conclusion shows that BBC news text classification model gets satisfying results on the basis of algorithms tested on the data set. The authors decided to show the comparison based on five parameters namely precision, accuracy, F1-score, support and confusion matrix. The classifier which gets the highest among all these parameters is termed as the best machine learning algorithm for the BBC news data set.