A Paretto Distribution based Gradient Boosting for Sustainable Agriculture

Hunger and insufficient diet cause reduced nutrients levels in an individual which is being the global issue. In ancient times people were exchanging products for survival, now every item is valued in terms of currency and supplied accordingly. Certain procedures lead to hoarding the products, making the food products unavailable in certain parts of the globe and in other parts it is of high cost to be affordable. Several technological processes were aligned with the notion of arriving increased productivity. This article analyzes the impact of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IOT) for sustainable agriculture. Parameters such as humidity of air, strength of infrasonic sound gathered using IoT are categorized with respect to precision parameters through various existing ML models. Then it is reiterated and achieves 93.23% of accuracy by deploying Pareto Distribution based Gradient boosting algorithm. Finally, it uses other ML frameworks to support supply chain management.