An Introduction to Agriculture 4.0

Precision Agriculture and Big Data

The third industrial revolution was shaped by automation and a shift from mechanical and analogue technology to digital electronics. Now, a fourth industrial revolution is emerging, marked by an expansion in machine learning and artificial intelligence enabled by Big Data. The potential for widespread impact is staggering across almost every industrial sector, and agriculture is no exception.

The convergence of agriculture and Big Data – a movement coined “Agriculture 4.0” at the World Government Summit – leverages new technologies such as the Internet of Things and Cloud Computing to introduce artificial intelligence into farming.


What is Precision Agriculture

Precision agriculture is one of the more popular terms used to capture all aspects of Big Data-enabled agricultural practices and digital/smart farms themselves. The United States Department of Agriculture (USDA) defines precision farming as “a management system that is information and technology based, is site specific and uses one or more of the following sources of data: soils, crops, nutrients, pests, moisture or yield, for optimum profitability, sustainability and protection of the environment”.

In the near future, virtually all stages of production will become digitally integrated. It’s this proliferation of Big Data in the agricultural sector, combined with user-friendly innovations designed to benefit farmers both big and small, that is poised to optimize our food system, increasing productivity and profitability for farmers while reducing harmful environmental impacts.

A slew of technological advancements have made precision agriculture feasible

Perhaps the most prominent technological advancement of all has been the yield map. Introduced in 1944, the yield map ushered in a new way to measure yields using GNSS-enabled maps. GNSS-based yield monitors quickly became the first precision agriculture technology to become widely adopted. These monitors provide farmers with a tool to collect site-specific information about their crop production and to generate maps (with estimates for sections as small as a few square yards) showing in-field variability on their farms.

After the advent of yield maps, adoption of precision farming technologies only continued to grow. In 2001, John Deere marked the beginning of another paradigm shift when the company made the decision to fit its tractors and other mobile machines with GPS sensors so they could be located anywhere on earth, with measurements down to the centimeter. This decision reduced fuel bills by as much as 40% and improved the uniformity and effectiveness of fertilizers, herbicides, and pesticides. Today, GNSS-based yield monitors and GPS-fitted tractors are commonplace.

Percent Adoption of Precision Agriculture Technologies

Other Transformative Technologies

  • GIS (Geographic Information Science/System) technologies

    A computer program that combines database management systems with graphics – data from a yield monitor is downloaded into GIS and converted into a color-coded yield map that displays yield levels throughout the field.

  • Remote Sensing

    The most common form of remote sensing is via electromagnetic radiation (detected by a sensor mounted in an airplane or satellite) – thermal-infrared radiation is emitted from plants at varying wavelengths, which can indicate whether plants are suffering from various stressors.

  • Soil Sampling

    Soil samples are collected at a large number of points across the field and statistically analyzed to provide an estimate of the soil’s physical and chemical properties at any given point in the field.

  • Electrical Conductivity Measurements

    Soil electrical conductivity can be measured either directly or through measurements of electrical inductance – these measurements are generally correlated with soil salinity, water content, soil density, topsoil depth, and clay content.

  • Variable Rate Technology (VRT)

    Maps of data from soil analyses, remote sensing, and measurements of electrical conductivity and pest and pathogen levels can be combined with yield map data to determine the factors underlying yield variability. Once these factors have been determined, variable-rate technology (VRT) equipment can be used to assist in determining how to vary the application rates of pesticides and fertilizers.

Global Precision Agriculture Market Segmentation

Farms Powered by Precision Agriculture Technology

The Role of Digital Farms (or "Smart Farms")

Digital/smart farms are enabled by precision agriculture, and are both more efficient and more sustainable than counterparts of the past.

‘Traditional’ farming is rife with complication and room for error – a farmer must constantly juggle a set of ever-changing variables, such as the weather, soil moisture levels and nutrient content, competition to crops from weeds, threats to crop health from pests and disease, and most importantly, the costs of taking action to deal with these threats.

Smart relieves some of this burden from farmers, freeing them to focus on the higher-order operational tasks of the farm.

The ultimate goal of a smart farm is twofold:

  1. To measure the variables going into the production chain as accurately as is cost-effective
  2. To relieve the farmer of as much of the burden of processing data as he or she is comfortable with yielding to a machine. Precision agriculture is what makes this possible.

Looking Ahead

Precision agriculture has ushered in a modern, high-tech branch of agriculture, one that has advanced so quickly it is now considered an entirely new industry: digital agriculture.

The digital agriculture industry will one day supersede the ‘traditional’ agriculture industry, although when this transition will finally be complete is contingent on a number of factors – notably barriers to entry such as the initial cost of adopting new technologies and the logistics of mass retraining.

Still, for innovators both large and small who are looking to venture into the burgeoning world of “Agriculture 4.0”, precision agriculture presents both exciting and unprecedented opportunities.



“The Smart Farm Initiative: The Future of Precision Agriculture.” The Aggie, 25 Apr. 2018,

December 12, Teena Maddox on, and 2018. “Agriculture 4.0: How Digital Farming Is Revolutionizing the Future of Food.” TechRepublic,

Wolfert, Sjaak, et al. “Big Data in Smart Farming – A Review.” Agricultural Systems, vol. 153, May 2017, pp. 69–80. ScienceDirect, doi:10.1016/j.agsy.2017.01.023.

Digital Agriculture | Cornell University Agricultural Experiment Station.

Advancing Agricultural Performance® and Environmental Stewardship. Digital Agriculture: Leveraging Technology and Information into Profitable Decisions. Dr. Matt Darr, Ag & Biosystems Engineering Some material adapted from "The Digital Transformation of Row Crop Agriculture" Authors: The Hale Group, Ltd & LSC International, Inc.

Finch, H. J. S., et al. “10 - Precision Farming.” Lockhart & Wiseman’s Crop Husbandry Including Grassland (Ninth Edition), edited by H. J. S. Finch et al., Woodhead Publishing, 2014, pp. 235–44. ScienceDirect, doi:10.1533/9781782423928.2.235.

“Digital Agriculture: Feeding the Future.” Project Breakthrough,

“Global Navigation Satellite Systems: Report of a Joint Workshop of the National Academy of Engineering and the Chinese Academy of Engineering” at NAP.Edu., doi:10.17226/13292. Chapter: Precision Agriculture: Opportunities and Challenges by Michael O'Connor

Plant, R., et al. “Precision Agriculture Can Increase Profits and Limit Environmental Impacts.” California Agriculture, vol. 54, no. 4, July 2000, pp. 66–71.

“The Future of Agriculture.” The Economist,

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