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.

翻译：本研究探讨了数据分析在作物种植中监测肥料施用的关键作用，不准确的施肥决策可能导致高昂的经济损失、阻碍粮食生产并造成环境危害。我们提出一种通过确定整个生长季所需肥料用量来预测养分施加的解决方案，该方案建议根据天气条件和土壤特性调整肥料施用量，以促进经济高效且环境友好的农业生产。收集的数据集具有高维度和异质性特征，本研究在决策制定过程中对大规模异构数据集进行考察，涵盖数据采集与分析环节。同时，我们以冬小麦作物为案例，研究了肥料施用与气象数据相结合对作物产量的影响。通过理解局部区域及地理因素，我们旨在稳定甚至降低农业养分需求的同时促进作物生长。经真实大规模数据集验证，所提方法被证明具有高效性和可扩展性。