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.
Using Focused Ultrasound and Pulsed Ultrasound as a Solution to Viral Infection
Viruses Both enveloped and non-enveloped viruses conceal their membrane-penetrating peptide, usually within a glycoprotein of the virion membrane, inside the coat, or within the virion lumen. Cellular signals expose membrane-penetrating peptides that influence the virus during its entry. Instances of cellular signals regulating virus entry include receptors, enzymes, and substances like proteases, metal ions, and reducing agents. Recently, motor proteins or virus maturation have been seen to regulate virus entry through mechanical processes.