IoT Machine Learning, and Cloud-Based Intelligent Soil Fertility Assessment: A Systematic Review

Abstract

India, like many other developing countries, has agriculture as one of its vital areas of socio-economic growth, and the traditional methods of farming are under great pressure because of the rise in population, weather changes, soil erosion and the scarcity of resources. This encourages the use of precision agriculture that incorporates the use of advanced technologies to optimize the production of crops and control of soils. Recent studies have highlighted the importance of AI, machine learning, IoT, and cloud computing in changing the way agriculture is done by allowing real-time monitoring of soils and making decisions based on data. The use of AI-based soil analysis and crop recommending systems with sensor networks to measure soil moisture, temperature, pH, nutrient, and pollutant levels have attracted a substantial amount of literature. These research illustrate better prediction of soil health, better irrigation timing and better recommendations of fertilizers. Nonetheless, most systems suffer the challenges of high computational cost, small field validation, reliance on big data and smallholder farming unscalability. Taken together, these literature sources point to an empty space in the research that would allow creating cost-effective, scalable, and user-friendly soil monitoring systems that can combine various parameters of soils into a single AI-based platform. The proposed study will help to fill this gap by creating an AI-based soil fertility assessment and crop recommendation system powered by sensor networks using IoT, cloud analytics, and deep learning models to improve the sustainability of agricultural productivity and soil health management.