Precision Agriculture Overview

Principles

Definition

Precision agriculture is an agricultural management approach and related research field that utilizes data and technology to observe, measure, and respond to spatial and temporal variability within fields. It investigates methods for optimizing resource use, enhancing productivity, and improving environmental outcomes through site-specific application of inputs and practices.

Ontological type

Core Technologies

Data Acquisition

Decision Support Models

Key developments

Key Figures

Process-Based Modeling era

R. A. Fischer [1] was active in this era with affiliations at University of Toronto [3] and University of California, Davis [4]. His 1985 paper 'Number of kernels in wheat crops and the influence of solar radiation and temperature' [7] advanced understanding of biomass accumulation and yield responses to environmental drivers, strengthening mechanistic crop models for site-specific management in this era. Compton J. Tucker [2] was affiliated with Colorado State University [5] and Texas A&M University [6] in this era. His 1985 paper 'Satellite remote sensing of total herbaceous biomass production in the senegalese sahel: 1980–1984' [8] contributed to the early integration of remote sensing with biomass estimation, enabling landscape-scale monitoring and informing irrigation and management decisions.

Satellite-Driven Scaling era

David Tilman[1] is a prominent ecologist whose work in the Satellite-Driven Scaling era spans the University of Washington[3] and the University of California, Santa Barbara[4]. David Tilman[1] contributed to framing scalable, data-informed approaches to agriculture and sustainability, as highlighted in Solutions for a cultivated planet[7], a work that underscored the importance of systems perspectives for large-area planning and resource allocation in this era. Gerrit Hoogenboom[2] advanced crop-system modeling through work at the University of Georgia[5] and the University of Florida[6], informing regional and global crop assessments during this era. Gerrit Hoogenboom[2] and the DSSAT cropping system model[8] together supported the integration of crop-growth and water-balance modeling for landscape-scale monitoring and precision-agriculture scaling across large extents in this era.

AI and IoT Integration era

Sergii Skakun [1] is associated with Park University [3] and the University of Maryland, College Park [4] during AI and IoT Integration era. His key contributions include the Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data [7], which demonstrated how deep learning can classify land cover and crop types from remote sensing data, enabling scalable mapping for precision agriculture. Mykola Lavreniuk [2] is affiliated with Taras Shevchenko National University of Kyiv [5] and National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute [6] during this era. As a co-author of the 2017 Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data [7], Lavreniuk contributed to advancing deep learning-based land cover and crop-type classification from remote sensing data, enabling more accurate, scalable mapping for precision agriculture in near-real-time decision support.