ThisresearchpaperexploresComputerVisionTechnologyinAgriculturalAutomation. Computer vision for precision agriculture has been the subject of considerableresearch interest in the past few years. To the uninitiated, precision agriculture is the farmingconcept based on monitoring, measuring, and responding to variability in crops. It aims tooptimize the returnswhile saving on the resources.Machine visionin agriculture isbeingwidely used to support precision agriculture via automated solutions. Machine vision can helpautomate arduous, repetitive tasks and deliver where humans fail. ML algorithms enable theanalysis of vast volumes of data accurately, offering a way to implement machine vision inagriculture. A subset of machine learning, deep learning uses an artificial neural network tounderstand information, identify patterns and learn while performing. The agricultural industryhas witnessed several contributions of computer vision-artificial intelligence (AI) models inareas such as planting, harvesting, advanced analysis of weather conditions, weeding and planthealthdetection and monitoring.