Harnessing Big Data in Agriculture by Addressing Heterogeneity in Large-Scale Data Mining Techniques and Limitations

This study harnesses big data in fertilizer nutrient research by addressing data heterogeneity and introducing novel data mining techniques, such as Slice Separate and Link (SSL), Layer Linking Stream (LLS), and Separate Order Connect (SOC). Our large-scale data, characterized by volume, velocity, variety, and value, encompasses agroclimatic indices and farming features. The techniques effectively manage feature subsets, handle multi-time series complexities, and provide accurate nutrient application timeline predictions. This research overcomes limitations in large-scale data analysis, optimizing fertilizer applications, promoting sustainable agriculture, and enhancing resource management. The experimental results demonstrate the potential of these techniques to significantly improve the accuracy and reliability of fertilizer application timeline predictions across diverse agroclimatic conditions and crop types.