Survey on the Integration of IoT and ML for Advancing the Agricultural Sector

Agriculture plays a vital role in the global economy, supplying essential food and raw materials to sustain billions worldwide. However, maintaining sustainable and productive farming practices requires precise, data-driven approaches. Traditional methods in agriculture often depend on broad and generalized guidelines, which overlook the nuanced differences in soil quality and conditions. This oversight can lead to inefficient fertilizer use and poor crop choices, ultimately reducing yields and harming the environment through excessive chemical applications. With technological advancements in the Internet of Things (IoT) and Machine Learning (ML), modern agriculture now has the tools to transform farming efficiency. Our project utilizes IoT sensors to monitor critical soil parameters—such as moisture, temperature, and macro nutrient levels (NPK)—in real time. These measurements are processed by machine learning models that continuously adjusts its crop and fertilizer recommendations based on the changing soil conditions. By integrating real-time data with intelligent algorithms, our project enables precision agriculture that not only minimizes fertilizer wastage but also optimizes crop health. Moreover, employing ensemble learning techniques improves the accuracy and adaptability of the system to account for varying soil types and environmental factors. Our solution directly addresses key challenges in modern agriculture, including inefficient fertilizer use, poor crop selection, and the lack of localized insights into soil health. In essence, our project offers a dynamic, data-driven approach to optimize crop recommendations and fertilizer usage, driving more sustainable and productive farming practices. Keywords: Smart farming, IoT, ML, Real-time analytics, Resource efficiency, Sustainable agriculture, Yield enhancement