Precision Agriculture Technologies Explained

No field is uniform. Soil texture, organic matter, nutrients, drainage, slope and pest pressure vary from place to place, so crop yield varies too. Conventional farming applies the same inputs everywhere, over-supplying some areas and under-supplying others. Precision agriculture, defined by the International Society of Precision Agriculture as a management strategy that uses data to support decisions according to estimated variability, aims to do the right thing, in the right place, at the right time and in the right amount.

The precision agriculture cycle

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1. Positioning and guidance

Global navigation satellite systems (GNSS), including GPS, GLONASS, Galileo, BeiDou and India’s NavIC, give machine positions. Accuracy levels:

• Standalone GNSS: about 1 to 3 m, adequate for mapping and scouting.
• SBAS corrections (such as GAGAN in India, WAAS, EGNOS): about 0.5 to 1 m.
• RTK (real time kinematic): about 2 cm, using a local base station or network.

Uses include auto-steering (reduces overlap and gaps, operator fatigue and night work errors), section control that switches sprayer or planter sections off over already treated areas or headlands, and controlled traffic farming, where machines always use the same wheel tracks to limit soil compaction.

2. Mapping variability

• Yield monitoring: sensors on combine harvesters measure grain flow and moisture, linked to GNSS position, producing yield maps that show which zones perform well or poorly year after year.
• Soil sampling: grid or zone sampling with laboratory analysis of pH, nutrients and organic matter.
• Soil sensors: electrical conductivity (EC) sensors, gamma radiometrics and optical sensors map texture and properties continuously.
• Topography from RTK or LiDAR identifies wet hollows and slopes.

3. Remote sensing and vegetation indices

Healthy vegetation reflects strongly in near infrared (NIR) and absorbs red light for photosynthesis. The normalised difference vegetation index combines the two:

$$NDVI = \frac{NIR - Red}{NIR + Red}$$

NDVI ranges from −1 to +1; dense, healthy crops are typically 0.6 to 0.9, stressed or sparse crops lower, and bare soil about 0.1 to 0.2. Satellites such as Sentinel-2 provide free 10 m imagery every few days; drones with multispectral cameras give centimetre resolution on demand. Other indices such as NDRE (red edge) are more sensitive in dense canopies. Thermal cameras detect water stress.

4. Variable rate technology (VRT)

Controllers on ISOBUS (ISO 11783) compatible tractors read prescription maps and adjust rates in real time:

• Variable rate seeding: higher density on productive soils, lower on poor ones.
• Variable rate fertiliser and lime: based on soil tests, yield potential or crop sensors that measure canopy greenness on the go.
• Targeted spraying: camera based “see and spray” systems detect weeds and spray only them, cutting herbicide use dramatically.
• Variable rate irrigation: centre pivots adjust nozzles by zone, linked to automated irrigation control.

5. Sensors, IoT and data platforms

Wireless soil moisture probes, weather stations, crop and livestock sensors and machine telematics send data to cloud platforms. Farm management information systems combine maps, operations records, costs and agronomic models. Machine learning, discussed in supervised vs unsupervised learning, helps predict yields, detect diseases from images and recommend actions.

6. Drones and robotics

Drones scout fields, map crops and increasingly spray pesticides and fertilisers on small plots and difficult terrain, subject to aviation and pesticide rules. Field robots weed mechanically or with lasers, and autonomous tractors are being trialled. Robotics overlaps with types of industrial robots and machine vision components.

Key formulas for precision agriculture

Benefits and challenges of precision agriculture

Benefits: higher input efficiency, lower costs, better yields in responsive zones, reduced nutrient and pesticide losses to water and air, less soil compaction, better records for traceability and certification, and reduced operator fatigue.

Challenges: equipment and subscription costs, connectivity in rural areas, data compatibility between brands, skills to interpret data, data ownership questions and small, fragmented fields in many countries. Services offered by cooperatives, start-ups and machinery hire centres help overcome these barriers.

Getting started

The key is to start with a clear question, such as where yields are lowest and why.

Begin with guidance and section control, which pay back quickly through input savings, then add yield mapping and soil sampling to understand variability, and move to variable rate application once zones are reliable. Water management through efficient methods such as those in drip vs sprinkler irrigation is another high value area for precision agriculture.

Precision agriculture interview and viva questions

1. What does RTK add to standard GNSS? Real time kinematic positioning corrects satellite signals with a nearby base station or network, improving accuracy from about 1 to 3 m standalone to about 2 cm.
2. Why does NDVI indicate crop health? Healthy vegetation reflects strongly in near infrared and absorbs red light for photosynthesis, so dense, healthy crops give NDVI of about 0.6 to 0.9 while stressed crops and bare soil give lower values.
3. What is a prescription map? It is a map of application rates by zone that a controller on an ISOBUS compatible tractor reads to adjust seed, fertiliser or spray rates in real time.
4. What is controlled traffic farming? Machines always use the same wheel tracks, guided by accurate GNSS, which limits soil compaction to those tracks.
5. Why must low NDVI zones be ground truthed before applying more nitrogen? Low NDVI can be caused by water stress, disease or soil problems rather than nitrogen shortage, so field checks are needed before changing the prescription.