Empowering agriculture with soil nutrient predictions: a data-driven approach

The novel arena of agricultural technology necessitates advanced modern methodologies for utilizing data to improve crop yield and optimize resource utilization. This research empowering Agriculture with Soil Nutrient Predictions using data-driven approach by employing a sturdy framework integrating machine learning along with comprehensive analysis of soil data to predict soil nutrient levels, assisting in performant agriculture. Employing a dataset with various parameters such as pH, nutritional composition, and mineral abundance of the soil, the analysis has performed by clustering the data types into distinctive groups of soil, which were further analysed to determine their nutrient profiles. K-Means clustering was employed to cluster the data representing the clustering into soil groupings that enlighten the qualities of soil in different kinds of farming. The predictive modelling utilizes Random Forest classifiers, to predict the categories. This approach lines well with the intention of IREx 2024, ICT, IoT, & Process Improvement and places a substantial emphasis on Educational Technology & Innovation and Operational Excellence. This research endeavours to establish new paradigms for agriculture to ensure sustainability in achieving the global target to food protection and restoring ecosystem health.