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AI Technologies in Modern Farming

F. JomonkulovaInstitute of Economics and Service,Department of Information Technologies of Samarkand,Samarkand,UzbekistanNarendra Singh BhandariGraphic Era Hill University Bhimtal Campus,School of Agriculture,UttarakhandZaid AlsalamiThe Islamic University of Al,College of Technical Engineering,Department of Computers Techniques Engineering,Diwaniyah,IraqAman SharmaGLA University,Department of Mechanical Engineering,MathuraNavdeep PrasharChandigarh Group of Colleges, Jhanjeri,Chandigarh Engineering College,Department of Electrical and Communication Engineering,Mohali,Punjab,India,140307Naveen NavalGraphic Era Deemed to be University,Department of Humanities & Social Sci.,Dehradun,India
2024en
ABI

Abstract

The combination of state-of-the-art revealing the many applications of machine learning in agriculture. The focal points—species identification, yield prediction, disease recognition, weed detection, and crop quality evaluation— address crop management. Livestock management is another aspect that addresses issues related to animal care and production. The research papers are divided into subcategories under the management of soil and water. The chosen articles have undergone thorough screening and categorization to demonstrate the many ways that machine learning technology may help the agricultural sector. It's interesting to note that farm management systems are evolving into AI-powered real-time applications that provide thorough guidance and insights obtained from learning techniques applied to sensor data, helping farmers make educated choices and take the necessary action.

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