Advancing Agriculture through Digital Soil Insight and Smart Farming Technologies
P. Lakshmanakumar *
Tractors and Farm Equipment, Centre of Excellence, R & D, Sembiam, Chennai, Tamil Nadu, 600011, India.
V. Prem
Tractors and Farm Equipment, Centre of Excellence, R & D, Sembiam, Chennai, Tamil Nadu, 600011, India.
M. Senthilkumar
Tractors and Farm Equipment, Centre of Excellence, R & D, Sembiam, Chennai, Tamil Nadu, 600011, India.
M. Suresh
Tractors and Farm Equipment, Centre of Excellence, R & D, Sembiam, Chennai, Tamil Nadu, 600011, India.
H. P. Vinodhkumar
Tractors and Farm Equipment, Centre of Excellence, R & D, Sembiam, Chennai, Tamil Nadu, 600011, India.
S. Gavin Larsen
Tractors and Farm Equipment, Centre of Excellence, R & D, Sembiam, Chennai, Tamil Nadu, 600011, India.
*Author to whom correspondence should be addressed.
Abstract
Digital soil insight plays a vital role in advancing agriculture by leveraging data collection and digitalization technologies to enhance smart farming practices. This review explores the integration of cutting-edge digital tools like remote sensing (RS), soil sensors and data analytics to enhance soil health monitoring, nutrient management and crop productivity. By utilizing real-time soil data, farmers can make informed decisions on irrigation, fertilization and pest control, leading to more effective and sustainable farming practices. The paper also examines challenges such as data accuracy, integration of heterogeneous datasets and the accessibility of digital tools for small-scale farmers. Furthermore, it highlights emerging trends in precision agriculture (PA), including artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT) technologies, which offer the potential to revolutionize farming practices. In the final analysis of this review provides insights into how digital soil insights are transforming agricultural systems, contributing to higher yields, reduced environmental impact and improved food security.
Keywords: Digital soil insight, smart farming, precision agriculture, data analytics, remote-sensing, sustainable farming