Smart Farming: What it is, How it is Done, and its Technologies

Every season, farm managers decide when to irrigate, where to scout and how much fertilizer a crop requires. Smart farming links those decisions to measured field conditions, connecting sensors, software and agronomic judgment in one routine. This article explains how it works, what it requires and how growers and agribusinesses can begin.

What is Smart Farming?

Smart farming is a data-informed approach to farm management in which digital technology supports each stage of the management cycle: sensing and monitoring, analysis and decision-making, and intervention (1). Smart farming technologies sit within the broader field of agriculture technologies; what defines the approach is how they connect field observations to decisions.

Precision agriculture centers on managing estimated spatial and temporal variability (2). Smart farming extends this by also responding to real-time events, such as a weather or disease alert (1). Automation and artificial intelligence are optional, not entry requirements.

How to Do Smart Farming?

The starting point is a management problem, not a device:

• Identify the problem: Name the decision that costs most when it goes wrong, such as irrigation timing.

• Assess practices and data: Note what is already recorded and what is missing.

• Select tools: Match each smart farming investment to a specific data gap.

• Establish a decision routine: Agree who reviews the data, how often and what triggers action.

• Review before expanding: Compare results at season end before scaling up.

In irrigation, sensors at several depths show how the root zone is changing: shallow readings track infiltration, while deeper readings track depletion of stored subsoil water (3). The grower weighs these readings against the forecast and crop stage, decides whether to irrigate, then checks how the profile responded.

What are the Components of Smart Farming?

Smart farm technology works as a chain, starting with data capture through sensors, satellites or manual observation (1). Connectivity moves that data to a platform, where software stores it and analytics compares readings against agronomic norms to flag deviations (1). People then interpret the result and decide on the intervention; automation can carry out some actions, but it is optional. A weak link, such as poor data quality or systems that cannot exchange data, limits the value of the whole chain (1).

What are the Benefits of Smart Farming?

A core benefit is timing: changes in soil or crop condition become visible as they develop, not at the next field visit. Measured data can support more appropriate irrigation and nutrient timing, targeted crop monitoring, planning based on recorded history, and structured records for traceability.

Each outcome is conditional. Results depend on data quality, correct sensor placement (3), sound agronomic interpretation, and local soil and climate. Collecting data does not, by itself, save water or raise yield; the decisions made with it do.

What are the Best Smart Farming Tools?

The best smart farming tools fit a defined management need. Criteria include the scale of the operation, field connectivity, compatibility with existing systems, budget, and the team's capacity to act on what the tools report.

IoT-Based Smart Farming Tools

IoT (Internet of Things) devices are connected field instruments that transmit readings automatically. Weather stations record air temperature, relative humidity, rainfall and wind, informing frost protection, spray timing and disease-risk assessment. Soil moisture sensors measure soil water content or tension at defined root-zone depths, informing irrigation timing.

This smart farming equipment takes point measurements, so placement determines how representative the data is. Mississippi State University Extension recommends installing sensors in an area with the field's dominant soil type, typical terrain and average yield (3).

Data-Driven Smart Farming Software

A smart farming app brings sensor data, field records and satellite imagery into one view, so a soil moisture trend can be read against irrigation history and the forecast. Satellite monitoring maps vegetation indices such as NDVI (Normalized Difference Vegetation Index), an indicator of canopy vigor, across entire fields.

NDVI cannot identify the cause of poor growth, and cloud cover interrupts imagery, so anomalies are confirmed by scouting (4). Decision-support software then converts the combined picture into alerts, records or recommendations.

How Does Doktar Support Smart Farming?

Doktar structures the approach as a connected chain from field measurement to decision. FieldStation measures soil moisture and soil temperature at multiple depths, plus air temperature and relative humidity, and delivers readings, low soil moisture alerts and frost alerts to the Doktar App. The app combines these readings with satellite-based crop health maps that flag zones for targeted scouting, weather risk alerts, and records of field activities and team tasks. As a smart farming company, Doktar pairs these tools with agronomic expertise, so decisions stay with the grower and rest on traceable field evidence.

Frequently Asked Questions About Smart Farming

Three practical adoption questions follow.

Is Smart Farming Suitable for Small Farms?

It can be, when adoption is incremental. Starting with one priority decision, such as irrigation timing on a high-value block, limits the upfront commitment. FAO points to connectivity, knowledge and skills, infrastructure and access to finance as conditions for adoption, especially among small-scale producers (5).

How Much Does It Cost to Implement Smart Farming?

Costs vary too widely for a single figure to be meaningful. The main drivers are hardware, subscriptions, installation, connectivity, maintenance and staff training. A practical test is whether a tool changes decisions often enough to justify its full running cost, not only its purchase price.

Can Smart Farming Help Reduce Water and Fertilizer Use?

It can contribute, but reductions are not guaranteed. Soil moisture data supports irrigating according to measured root-zone conditions rather than a fixed calendar, and soil analysis informs fertilizer source and rate, which, with timing and placement matched to crop demand, form the basis of 4R nutrient stewardship (6). Actual savings depend on baseline practices, soil and climate.

Smart farming delivers results when measurement, interpretation and action operate as one routine. Doktar's connected portfolio of field hardware and farm management software is engineered to sustain that routine across every season.

Reference(s):

(1) Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023

(2) International Society of Precision Agriculture. (2019, July). ISPA official definition of precision agriculture. ISPA Newsletter, 7(7). https://ispag.org/site/newsletter/?id=90

(3) Rix, J., Lo, H., Gholson, D., & Henry, M. (2024). Irrometer Watermark series: Location selection (Publication 3539). Mississippi State University Extension Service. https://extension.msstate.edu/publications/irrometer-watermark-series-location-selection

(4) Mulla, D. (2020, December 29). Trends in satellite remote sensing for precision agriculture. Crops & Soils, 54(1). American Society of Agronomy, Crop Science Society of America and Soil Science Society of America. https://www.sciencesocieties.org/publications/crops-soils/2021/january-february/trends-in-satellite-remote-sensing-for-precision

(5) Food and Agriculture Organization of the United Nations. (2022). The State of Food and Agriculture 2022: Leveraging agricultural automation for transforming agrifood systems. Key messages. FAO. https://www.fao.org/3/cb9479en/online/sofa-2022/key-messages.html

(6) Liu, G., Morgan, K. T., Li, Y., Zotarelli, L., Wang, Q., & DeValerio, J. (2015). What is 4R nutrient stewardship? (HS1264). UF/IFAS Extension. https://doi.org/10.32473/edis-hs1264-2015