Enhancing Precision Agriculture: IoT-Enabled Soil Nutrient Analysis and Deep Learning-Based Crop Recommendation Models

Abstract

The emergence of the Internet of Things (IoT) has brought about a revolution in a number of fields, most notably agriculture, where precision farming techniques that improve sustainability and production have been made possible. In the context of precision agriculture, this review paper focusses on the integration of IoT technology for crop recommendation models and soil nutrient analysis. Farmers can accurately determine nutrient requirements and levels by using real-time soil health monitoring made possible by IoT-enabled sensors and data analytics. This study conducts a thorough analysis of the body of research on the several Internet of Things applications that support efficient crop management and soil nutrient optimisation. It examines cutting-edge sensor technologies—such as spectrometry and electrochemical sensors—used in soil monitoring and emphasises how important it is to get precise soil data. The study also looks at the creation of predictive models that use machine learning algorithms to suggest appropriate crop varieties depending on environmental factors and soil nutrient profiles. The results highlight how the Internet of Things (IoT) may help make better decisions in agriculture, which will eventually increase crop yields and resource efficiency. The knowledge gained from this review not only advances our understanding of precision agriculture enabled by the Internet of Things, but also opens up new avenues for future research focused on combining cutting-edge technology to address the problems facing contemporary farming methods.