Smart technologies for determining water flow in irrigation systems

Abstract

Without the need for hand-coding, Machine Learning helps systems to enhance and develop dynamically from their experiences. As a result, numerous tech firms have been creating Artificial Intelligence applications in recent years. The majority of irrigation systems available today allow customers to program them to provide a specified amount of water at specific times. On the other hand, a garden frequently has a variety of plants, each of which needs a varying amount of water. This research planned an irrigation system that uses deep learning to regulate the quantity of water given to each type of plant based on plant identification to address this problem. The software and hardware are the two primary constituents of the technology. The former is linked to cameras for plant identification and uses a database to determine the appropriate amount of water; the other regulates the amount of water that can flow out. The technology is designed to predict how long to water the plants after discovering the perfect soil moisture with the applications and incorporating it with the outcome of the existing soil moisture level with the Arduino. This will allow the program to modify the software in the irrigation system controller to alter the period of time the regulator should be kept open.

Study of kinetical laws of process of the catalytic synthesis of nanocarbon from methane

“…The phase composition of the nanocarbon was analyzed using a semi-quantitative method using X-ray diffractograms. The morphology and structure of the product were studied under a microscope brand JEM-1400 (JEOL, Japan) using electron illumination microscopy (accelerating voltage-40 to 120 kV, cathode-tungsten), as well as using a scanning electron microscope brand JSM-7001F (JEOL) [28][29].The specific surface area of the samples was determined by the thermal desorption method of nitrogen.…”