The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture

• Published in: Agriculture (Basel) Vol. 13; no. 8; p. 1593
• Main Authors: Karunathilake, E. M. B. M., Le, Anh Tuan, Heo, Seong, Chung, Yong Suk, Mansoor, Sheikh
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Agricultural industry; Agricultural production; agricultural productivity; agricultural technology; Agricultural wastes; Agriculture; Automation; Big Data; big data analytics; cost effectiveness; Crop yield; Crop yields; Data analysis; Data management; Decision making; Digital agriculture; Environmental effects; Environmental impact; Farming; Global positioning systems; GPS; Information management; innovation adoption; Innovations; Internet; Internet of Things; Internet of Things (IoT); Machine learning; Management decisions; Precision agriculture; precision farming; Productivity; Sensors; smart farming; Sustainability; Technology utilization; wastes; World population; United States > US
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture13081593

Precision agriculture employs cutting-edge technologies to increase agricultural productivity while reducing adverse impacts on the environment. Precision agriculture is a farming approach that uses advanced technology and data analysis to maximize crop yields, cut waste, and increase productivity. It is a potential strategy for tackling some of the major issues confronting contemporary agriculture, such as feeding a growing world population while reducing environmental effects. This review article examines some of the latest recent advances in precision agriculture, including the Internet of Things (IoT) and how to make use of big data. This review article aims to provide an overview of the recent innovations, challenges, and future prospects of precision agriculture and smart farming. It presents an analysis of the current state of precision agriculture, including the most recent innovations in technology, such as drones, sensors, and machine learning. The article also discusses some of the main challenges faced by precision agriculture, including data management, technology adoption, and cost-effectiveness.