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Machine Learning in Agriculture: A Comprehensive Updated Review.

Lefteris Benos¹, Aristotelis C Tagarakis¹, Georgios Dolias¹

¹Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece.

Sensors (Basel, Switzerland)

|June 2, 2021

Summary

Machine learning in agriculture offers significant potential for data-driven farming. This review highlights its application in crop, water, soil, and livestock management, emphasizing artificial neural networks for enhanced efficiency.

Keywords:

artificial intelligence crop management livestock management machine learning precision agriculture precision livestock farming soil management water management

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Area of Science:

Agricultural Science
Computer Science
Data Science

Background:

Digital transformation is revolutionizing agriculture, integrating artificial intelligence (AI) for data-driven decision-making.
Machine learning (ML), a subset of AI, presents a powerful approach to address challenges in establishing knowledge-based farming systems.
The increasing volume and sources of agricultural data necessitate advanced analytical techniques.

Purpose of the Study:

To systematically review and synthesize recent scholarly literature on machine learning applications in agriculture.
To investigate the use of machine learning in crop, water, soil, and livestock management.
To identify trends, popular algorithms, and data sources within agricultural machine learning research.

Main Methods:

A comprehensive literature review adhering to PRISMA guidelines.
Searches were conducted using keyword combinations: "machine learning" with "crop management", "water management", "soil management", and "livestock management".
Eligible studies were limited to journal papers published between 2018 and 2020.

Main Results:

The integration of machine learning in agriculture spans multiple disciplines, fostering international convergence research.
Crop management emerged as the most researched area, with Artificial Neural Networks (ANNs) proving highly efficient among various ML algorithms.
Maize, wheat, cattle, and sheep were the most frequently studied subjects, utilizing data from sensors on satellites and unmanned vehicles.

Conclusions:

Machine learning holds substantial promise for advancing agricultural practices through intelligent data analysis.
The study underscores the need for systematic research and highlights the benefits of ML for stakeholders in the agricultural sector.
Continued research and adoption of ML technologies are crucial for enhancing agricultural efficiency and sustainability.