Revolutionizing Potato Agriculture: Harnessing Machine Learning Techniques for Disease Detection and Management
Aim: The aim of this study is to make a disease-predicting model trained on data from weather stations and API using machine learning that gives the farmer the ability to predict crop diseases before they set in, allowing them to take timely preventative measures and reduce wastage. Materials and Methods: In this study the Internet of Things (IoT) sensors throughout agricultural fields of potato crops in Jafferabad, Depalpur Punjab. The sensors collect real-time data on environmental conditions, such as precipitation, air temperature, relative humidity, wind speed, and direction, Dew Point, VPD, and the Delta T values, to identify subtle disease indicators and patterns within the environmental data. Our novel machine-learning program makes use of the data collected by the weather station and analyses them. Results: Using the data, one predictive statistical method using Python 3.8.0 was created which uses the data from the weather station which can predict diseases before they set in.
Nanoparticles and Aqueous Amine-Based Formulation to Develop CO2 Foam for Sequestration and Oil Recovery
Carbon dioxide (CO2) is an important greenhouse gas that helps trap heat in our atmosphere; without it, our planet would be inhospitably cold [1]. It is the fourth most abundant gas in the Earth's atmosphere. It is a byproduct of normal cell function when breathed out of the body, and produced when fossil fuels and organic wood compounds are burned [2]. However, an increase in CO2 concentration in the atmosphere can contribute to climate change and ocean acidification, and exposure to high levels of CO2 can produce a variety of health effects [3]. Human progress and economic innovation have led to increased emissions, causing climate change and affecting all living creatures. Current levels are 36.8 Gt CO2 in 2023 and are expected to reach 54-56 Gt CO2 by 2030 [4]. Figure 1 displays the current atmospheric CO2 measurements at Mauna Loa Observatory without seasonal variations [5].