SOIL NUTRIENT PREDICTION AND CROP PREDICTION RECOMMENDATION SYSTEMS USING IOT AND AI TECHNIQUES: CURRENT TRENDS AND CHALLENGES

The emergence of the Internet of Things (IoT) and Artificial Intelligence (AI) technologies has transformed the agricultural industry by offering promising solutions to issues relating to crop recommendation and soil nutrient prediction systems. Due to the urgent need for sustainable agricultural practices, IoT and AI-based soil nutrient prediction and crop recommendation systems have drawn significant attention recently. Recent advancements have introduced innovative models capable of monitoring soil health, predicting nutrient deficiencies, and recommending suitable crop varieties tailored to a specific environment with varying climatic conditions. This paper presents recent developments in IoT and AI-driven technologies that improve smart farming by providing data-driven support and real-time monitoring of soil health. AI models are capable of analysing data from sensors, satellite imagery, and past history to accurately predict soil nutrients and recommend crops, ensuring efficient and sustainable agricultural practices. Despite the advancement in technology, data quality, model interpretability, cost, and accessibility pose challenges that hinder the widespread adoption, particularly among smallholder farmers. The future of smart farming systems lies in overcoming existing barriers and advancing technology to offer scalable, affordable, and user-friendly solutions. Various methodologies and approaches, such as hybrid and ensemble models that combine data-driven AI methods with domain-specific agronomic knowledge, have demonstrated improved reliability and accuracy. With emphasis on some of the important soil parameters such as nutrients, moisture, power of Hydrogen, temperature, relative humidity, and electrical conductivity, this paper discusses the roles of IoT and AI in enhancing the efficiency and precision of Smart Farming. Furthermore, this paper provides insight into current trends and techniques in Smart Farming by synthesising findings from a range of studies on advanced technologies.