Enhancement of the Performance and Accuracy of Soil Moisture Data Transmission in IOT

Abstract

The need for soil moisture data transmission in IoT is growing thus there is a need to improve the accuracy and performance of such IoT systems. The focus of this research is on soil moisture that is detected by IoT sensors. IoT sensor data is collected and transmitted to the farmer or user. Using deep learning, actuators can take be trained to take remedial action in case of need, such as the sprinkling of water in the agricultural environment. The main focus of research is to measure data and enhance accuracy and performance. Sensors are used to monitor and record every aspect of the environment in their respective settings. The most commonly utilized sensors include proximity sensors, temperature sensors, smoke sensors, etc. Since human population, industry, and energy consumption are all expected to rise in the next decades, environmental monitoring tools and applications must be upgraded to be more accurate and efficient. The detection and measurement of soil moisture is the initial step for smart agriculture. With the use of agricultural control centers, farmers may identify crops in need of treatment and figure out the best quantity of water, fertilizer, and pesticides to administer based on sensor data and imagery input. For farmers, this ensures the soil gets the proper quantity of chemicals for optimal health, while also cutting expenses and minimizing environmental effects, all while reducing waste. The objective of the research work is to improve the accuracy and performance when soil moisture data is transmitted over IoT devices.