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How Machine Learning has Changed The Agriculture Sector? - Review
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
With the development of big data technologies and high-performance computers, machine learning has opened up new possibilities for data-intensive science in the multidisciplinary field of agri-technology. This study provides a thorough analysis of applications-focused research of artificial intelligence in systems that produce food. The analysed works were divided into the following categories: (a) crop management, which includes applications on yield prediction, disease detection, and weed identification Crop quality and species identification; b) animal welfare and livestock production applications; c) water management; and d) soil management. The categorization and filtering of the articles shown here show how machine learning technologies will help agriculture. Farm management systems are developing into real-time artificial intelligence enabled programmes that offer detailed recommendations and insights for farmer decision support and action by using machine learning to sensor data.
How to cite this paper
@article{1704567,
author = {Dandu Sai Satya Vamsi Krishna Raju, Ediga Vinod Kumar, Ediga Mahendra goud, Sapna R},
title = {How Machine Learning has Changed The Agriculture Sector? - Review},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {11},
pages = {778-783},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704567.pdf},
abstract = {With the development of big data technologies and high-performance computers, machine learning has opened up new possibilities for data-intensive science in the multidisciplinary field of agri-technology. This study provides a thorough analysis of applications-focused research of artificial intelligence in systems that produce food. The analysed works were divided into the following categories: (a) crop management, which includes applications on yield prediction, disease detection, and weed identification Crop quality and species identification; b) animal welfare and livestock production applications; c) water management; and d) soil management. The categorization and filtering of the articles shown here show how machine learning technologies will help agriculture. Farm management systems are developing into real-time artificial intelligence enabled programmes that offer detailed recommendations and insights for farmer decision support and action by using machine learning to sensor data.},
month = {May},
}