Barriers to Implementing Computational Intelligence-Based Agriculture System

This study thoroughly examines existing literature to examine the challenges that hinder the proper deployment of computational intelligence in agriculture (CIA), considering its growing significance as the globe aims for zero hunger by 2030 per the United Nations. By conducting a comprehensive analysis of various scholarly sources, we have classified the obstacles encountered by stakeholders in the agriculture sector into different categories. These categories include economic, technological, social, and regulatory dimensions. This categorization aims to offer readers a comprehensive comprehension of the complex challenges faced by stakeholders in the agriculture industry. The synthesis of results from several sources within each category permits a thorough depiction of the problems present. In this research study, we offer an even more thorough analysis of the numerous components that add to these barriers. This gives a clearer understanding of the complex relationship between economic restraints, modern technology restrictions, and social and governing dynamics that either heighten or alleviate these challenges. The analysis shows similar tendencies and emergent concerns from such limits. These concerns must be addressed to guarantee AI-driven farming system reliability. This project analyzes agricultural data to help policymakers, academics, and experts face future difficulties. Finally, this study stresses problem-solving, resource recognition, and novel ideas to make farming smarter and more sustainable.