Optimization of ML techniques with Gaussian Quadrature Numerical Analysis for Precision Agricultural Augmentation

Abstract

Agriculture is a significant and vivacious domain in the fiscal evolution of the globe. With current population, climatic conditions and resources, agriculture turns out to be a challenging task to fulfill the requirements of the future population. Intelligent Precision agriculture also known as intelligent smart farming has emerged as an innovative tool to address current challenges in automated agricultural sustainability. This mechanism that drives this cutting edge technology, that is the machine learning (ML) giving the machine ability to learn without being explicitly programmed reinforced with rewards. AI and ML together with IoT (Internet of Things) enabled farm machineries are key components of the future agriculture revolution ahead. In this work, a systematic Gaussian Quadrature numerical analysis of ML applications in the field of agriculture is done. Fixing the right real-time problems followed by solving it for agricultural augmentation or amplification thereby leading to global best.