Machine Learning for Dynamic Management Zone in Smart Farming

Due to economic and logistic Agriculture 4.0 is using many modern research and technologies reasons, soil sampling are not frequent enough to understand in different aspects of agriculture including genomics, nanotechnology, its impact on annual yield. For example P, K, Mg are tested synthetic proteins, Internet of Things, automation once for three years. However, altitude, soil texture data are not and machine learning [1]. As an important pillar in this space, changed or changed slowly. Based on our data management experience data-driven agriculture has gain a momentum in last twenty in UK farms, yield maps are being collected by many years as a retrofitting mechanism for the available technologies farmers in the last two decades. Most of the analyses have been to feed 9 billion population in 2050. It has become more realistic focused on spatial variability of individual maps. Due to lack of than ever due to wider use of sensors, cloud computing and consecutive number of yield maps and crop rotation complexities, their integration with cyber-physical-social farming systems to both spatio-temporal analysis has been limited so far [? ]. use big data for intuition, intelligence and insights. However, Therefore, many farmers, agronomists and scientists are interested data-driven agriculture is challenging for small actors but important in looking at the relations of those data layers, deriving for global sustainability compared to others industries compound new data layers and accordingly make site-specific such as healthcare, fin-tech and manufacturing.