# Machine Learning-based Nutrient Application's Timeline Recommendation for Smart Agriculture: A Large-Scale Data Mining Approach

arXiv: https://arxiv.org/abs/2310.12052 · HTML: https://mlsanity.com/paper/2310.12052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 36 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Builds on (most cited first, 1 of 1)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)

## Abstract (arXiv)

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.