Machine Learning-based Nutrient Application's Timeline Recommendation
  for Smart Agriculture: A Large-Scale Data Mining Approach
Usama Ikhlaq Tahar Kechadi
This study addresses the vital role of data analytics in monitoring
fertiliser applications in crop cultivation. Inaccurate fertiliser application
decisions can lead to costly consequences, hinder food production, and cause
environmental harm. We propose a solution to predict nutrient application by
determining required fertiliser quantities for an entire season. The proposed
solution recommends adjusting fertiliser amounts based on weather conditions
and soil characteristics to promote cost-effective and environmentally friendly
agriculture. The collected dataset is high-dimensional and heterogeneous. Our
research examines large-scale heterogeneous datasets in the context of the
decision-making process, encompassing data collection and analysis. We also
study the impact of fertiliser applications combined with weather data on crop
yield, using the winter wheat crop as a case study. By understanding local
contextual and geographic factors, we aspire to stabilise or even reduce the
demand for agricultural nutrients while enhancing crop development. The
proposed approach is proven to be efficient and scalable, as it is validated
using a real-world and large dataset.