Enhancing precision agriculture: A comprehensive review of machine learning and AI vision applications in all-terrain vehicle for farm automation

Abstract

The automation of all-terrain vehicles (ATVs) through the integration of advanced technologies such as machine learning (ML) and artificial intelligence (AI) vision has significantly changed precision agriculture. This paper aims to analyse and develop trends to provide comprehensive knowledge of the current state of ATV-based precision agriculture and the future possibilities of ML and AI. A bibliometric analysis was conducted through network diagram with keywords taken from previous publications in the domain. This review comprehensively analyses the potential of machine learning and artificial intelligence in transforming farming operations through the automation of tasks and the deployment of all-terrain vehicles. The research extensively analyses how machine learning methods have influenced several aspects of agricultural activities, such as planting, harvesting, spraying, weeding, crop monitoring, and others. AI vision systems are being researched for their ability to enhance precise and prompt decision-making in ATV-driven agricultural automation. These technologies have been thoroughly tested to show how they can improve crop yield (15-20%), reduce overall investment (25-30%), and make farming more efficient (20-25%). Examples include machine learning-based seeding accuracy, AI-enabled crop health monitoring, and the use of AI vision for accurate pesticide application. The assessment examines challenges such as data privacy problems and scalability constraints, along with potential advancements and future prospects in the field. This will assist researchers and practitioners in making well-informed judgments regarding farming practices that are efficient, sustainable, and technologically robust.