Evaluation of Soil Quality Utilising a Deep Learning Model for an Internet of Things-Enabled Smart Agriculture System

Soil quality evaluation and management have taken a new turn with the advent of intelligent agricultural systems that combine deep learning models with internet of things technologies. This article summarises previous research on agricultural systems that have used deep learning models to evaluate and improve soil health via the use of the Internet of Things (IoT). Sensors connected to the Internet of Things (IoT) allow for the constant tracking and analysis of vital soil factors like temperature, moisture, pH, and nitrogen, phosphorus, and potassium (NPK) levels. With the use of deep learning methods like CNNs, RNNs, and DNNRs, data processing, soil quality forecasts, and resource management decisions made in real-time are immensely facilitated. With an emphasis on automated data collecting, precise soil categorisation, and the optimisation of irrigation and fertilisation processes, the research highlights substantial progress in soil quality assessment. Systems that use deep learning algorithms can quickly detect and proactively manage soil health concerns including nutrient shortages, salinity, and deterioration. By increasing agricultural yields while decreasing water consumption, enhancing crop yields with less chemical inputs, and implementing IoT-based smart agriculture solutions, environmental sustainability is improved.