Research Topic · Peer-Reviewed

Machine Learning in Precision Agriculture

Precision agriculture is an advanced farming practice that utilizes modern technologies, such as machine learning and remote sensing, to improve efficiency and productivity in crop production. Machine learning is used to process large amounts of data from various sources, such as satellites and soil sensors, to help…

Curated from this journal's research 📚 2 peer-reviewed articles cited Cited 15× across the literature 🔖 ISSN 2998-1506 🗓 Reviewed June 2026

Overview

Precision agriculture is an advanced farming practice that utilizes modern technologies, such as machine learning and remote sensing, to improve efficiency and productivity in crop production. Machine learning is used to process large amounts of data from various sources, such as satellites and soil sensors, to help farmers identify and monitor trends in crop growth. Machine learning can also be used to detect potential issues such as disease outbreaks and pest infestations, enabling early interventions that can reduce crop loss and yield decline. This technology can also be used to reduce input costs, improve resource utilization, and optimize irrigation and fertilization cycles to boost crop yields. By using machine learning in precision agriculture, farmers can increase the productivity and profitability of their operations while reducing their environmental impact.

Research published in this journal

2 peer-reviewed articles, ranked by relevance. Each links to its DOI.

How this research is being cited

The 2 articles above have been cited 15 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

A sample of recent works citing this journal's research on Machine Learning in Precision Agriculture, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in Precision Agriculture (ISSN 2998-1506).

This page summarises published research for orientation; it is not medical or professional advice.