An intelligent IOT sensor coupled precision irrigation model for agriculture

Abstract

Sustainable agricultural practices are required, especially for irrigation, to provide for a growing population. About 85% of the freshwater resources in the world are used for irrigation. ''Us,'' current irrigation procedures must be either updated or replaced with cutting-edge, intelligent systems that utilize the ML, IoT (Internet of Things), and sensor networks which are wireless. In the proposed paper, a brainy system for tracking and scheduling accuracy irrigation using IoT, a LoRa-based machine learning (ML) is being introduced. We developed an independent irrigation technique that would provide the tomato and eggplant with the precise amount of water they required was set up using the information obtained from the soil moisture sensors. In comparison to traditional watering, which required 7541 mL for the banana plant and 8755 mL for the rice plant, the irrigation system irrigated the plants with an overall quantity of 5010.745 mL & 4421.09 mL in one month. Overall, this led to a 46% reduction in water usage, and the plants looked better than they would have with conventional watering. The simulation results clearly show that the suggested approach uses water more sensibly than cutting-edge models in the experiment farming region.

Keywords

• Rice plant
• Watering model
• Banana plant
• Farming
• Irrigation
• LSTM