Machine Learning in Agriculture: A Review

Clicks: 389
ID: 109954
2018
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #112 of 1,694 articles by views in sensors

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,694 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.
Reference Key
liakos2018sensorsmachine Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Konstantinos G. Liakos;Patrizia Busato;Dimitrios Moshou;Simon Pearson;Dionysis Bochtis;Liakos, Konstantinos G.;Busato, Patrizia;Moshou, Dimitrios;Pearson, Simon;Bochtis, Dionysis;
Journal sensors
Year 2018
DOI
10.3390/s18082674
URL
https://www.mdpi.com/1424-8220/18/8/2674/htm##fig_body_display_sensors-18-02674-f002
Keywords
artificial intelligence planning water management precision agriculture soil management crop management livestock management National Center for Biotechnology Information NCBI NLM MEDLINE review pubmed abstract nih national institutes of health national library of medicine pmid:30110960 pmc6111295 doi:10.3390/s18082674 konstantinos g liakos patrizia busato dionysis bochtis

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.