Prevention of soil erosion, prediction soil NPK and Moisture for protecting structural deformities in Mining area using fog assisted Smart agriculture system
Surajit Mohanty, Subhendu Kumar Pani, Niva Tripathy, Raghunath Rout, Mousumi Acharya, Prakash Kumar Raut
Abstract
A new idea called "smart agriculture" enables farms to be managed using cutting-edge technology like IoT, AI, robotics, drones, etc. The goal is to maximize the use of the resources at hand while producing more crops of higher quality and quantity. Crop output growth is mostly dependent on the fertility and moisture content of the soil. In the past, different fertilizers were added to the soil to boost its nutrients, but this process took time and labour from humans. Subsequently, as technology advanced, the data gathered from IoT devices was processed via cloud computing. Fog computing provides a solution to the significant latency associated with cloud server location that is remote. The main goal of this research is to create a fog-assisted recommendation model that can be applied to the future prediction of soil moisture content and NPK. When combined with agricultural systems, machine learning algorithms provide a proactive, data-driven method for maximizing resource management and soil health. It offers a succinct overview of how to forecast crop growth through the use of hybrid machine learning algorithms on the IFogsim platform to enhance soil quality and irrigation efficiency. Moreover, it lessens soil erosion and increases soil stability. In the mining sector, the suggested technique is helpful for protecting against structural deformities and stabilizing slopes.