How Analog Devices brings inertial discipline to precision agriculture.
Agriculture has entered the era of continuous PNT.
Precision agriculture is moving toward full automation. Guidance systems once treated GNSS as the entire solution; today, the industry recognizes that satellite signals are necessary but insufficient. Farms have become complex RF environments. Tree canopy, terrain, outbuildings, seasonal geometry shifts, multipath near grain elevators, interference from adjacent equipment, and the simple reality that tractors roam in and out of open-sky visibility all challenge the idea that GNSS alone can sustain continuity.
OEMs are building guidance systems that must keep machines on path even when GNSS falters. Autonomy depends on uninterrupted perception of position, velocity and attitude. That means pairing GNSS with inertial systems engineered for agricultural machines, not adapted from other domains.
In a recent conversation with Inside GNSS, Tzeno Galchev, Director, Product Marketing and Applications Engineering for Analog Devices, Inc. (ADI), described how their inertial measurement units (IMUs) are being integrated into next-generation tractors, implements, drones and robotics platforms. ADI’s engineers are focused on what really matters in the field: disciplined inertial performance, controlled lifetime drift, rugged packaging and reliable sensor fusion with GNSS. The message was unambiguous: Autonomy in agriculture can scale rapidly when inertial becomes a baseline requirement.
The Market Reality: Why Inertial Matters Now
Precision agriculture has matured beyond the first decade of “straight-line” GNSS guidance. Machines now operate in a wider set of field geometries, crop types and environmental constraints. Several forces are converging:
Tractors are evolving from operator-assisted systems to autonomy-ready platforms. Implements are following, including precision planters, high-clearance sprayers, and robotic harvesters. Each requires continuous PNT. A single GNSS dropout during an autonomous end-of-row turn can result in overlap, missed coverage or unsafe behavior.
Agriculture spans open sky areas and GNSS-hostile corridors. Machines pass under tree rows, within orchard canopies, beside barns or silos, or along field edges lined with windbreaks. Modern high-value crops, such as vineyards, orchards and berries, introduce dense canopy that disrupts L-band signals. Even row crops can create directional multi-path in late summer.
OEM Pressure to Deliver “Always-on” Paths
Agricultural OEMs face customer expectations shaped by the automotive sector. The question is no longer whether GNSS can deliver accuracy; it is whether the total system delivers continuity. That continuity is now a competitive differentiator. Dead-reckoning performance, not positional Root Mean Square (RMS) in open sky, shapes the user experience.
Cost Realism and the Mid-Market Explosion
Farm sizes vary globally. Not every user can justify aerospace-tier inertial systems. ADI’s view is that precision agriculture needs inertial performance that respects cost boundaries while still meeting the dynamics of field machinery: vibration, temperature cycling and shock.
“The demand is there because there’s a shortage of workforce, especially in the developed countries, and these machines make a considerable difference in the cost and efficiency of farming operations,” Galchev said. “They are replacing and reducing the number of workers needed as well as putting workers out of harm’s way.”
The Shift to Autonomy-Grade Attitude Estimation
GNSS provides position and velocity; but many operations require continuous knowledge of roll, pitch and yaw. Sprayers use boom leveling. Planters need implement attitude to maintain depth accuracy. Drones require stable orientation in low-signal environments. INS establishes those states even when GNSS is degraded.
THE RESULT: GNSS remains the reference, but inertial is now the mechanism that closes the reliability gap.
Inertial Basics for Agricultural Platforms
Agricultural operators rarely see inertial systems directly. They see better lines, fewer skips, improved boom stability, and smoother turns. Under the hood:
• IMUs measure angular rate and acceleration along orthogonal axes.
• Sensor fusion in an inertial navigation system (INS) uses those measurements to propagate position, velocity and attitude during GNSS gaps.
• Drift is inherent, but it can be minimized, modeled and constrained with well-tuned sensor fusion.
• GNSS resets the INS, bounding cumulative error.
• Agricultural use-cases emphasize short-to-medium duration bridging, not long-haul independent navigation.
Modern MEMS technology has reduced noise, bias instability, and temperature sensitivity to levels appropriate for automotive-grade and robotic applications. ADI’s work has focused on improving consistency across production units, strengthening environmental robustness, and integrating compensation routines at the firmware level.
Agricultural machinery introduces several complicating factors that inertial systems must handle cleanly:
• High vibration environments from diesel engines, tillage tools, and PTO-driven implements.
• Complex motion during headland turns, uneven terrain and differential traction events.
• Thermal swings, from dawn cold starts to midday heat.
• Mechanical shock, especially on implements.
• Long duty cycles, including 14 to 18 hour days in planting or harvest season.
This environment is less deterministic than automotive and more dynamic than many robotics platforms. The IMU/INS must treat vibration as a feature of the mission, not a source of error.
ADI’s Technical Approach
ADI designs inertial solutions with a focus on predictable error behavior, rugged packaging and stable sensor fusion. The company emphasizes several technical principles:
VIBRATION TOLERANCE. Farm machinery produces persistent broadband vibration. ADI considers how vibration intrinsically disturbs the sensors and ADI engineers design mechanical structures that better suppress, cancel and otherwise reduce the effect of vibration directly into the MEMS structures themselves because once vibration is allowed to pollute the sensor signal, it is too late for the INS system to do anything about it. This ensures the INS maintains the correct angular-rate and acceleration signatures even when implements shake violently.
BIAS REPEATABILITY. This is the lifetime bias drift expectation that intends to capture all unmodeled error sources and is not commonly specified in MEMS IMU datasheets. It provides a single error window that will determine the convergence times for critical estimation/filter loops. For systems that need to turn and deploy quickly, failure to anticipate and quantify these errors can limit deployment time and degrade initial heading accuracy. In their latest products, ADI has expanded their Bias Repeatability definition to include turn-on drift/settling, drift from package stress relief, electronic drift and thermal hysteresis. In parallel with expanding the coverage of this specification, ADI has reduced this metric by an order of magnitude in recently-released devices, such as the ADIS16545 and ADIS16576.
AXIS-TO-AXIS ALIGNMENT. With tight axis-to-axis alignment out of the box and calibrated through an extensive inertial routine over multiple temperature set-points, tight alignment can be achieved only using mechanical alignment features. For tighter alignment than 0.25° one could leverage the tight axis-to-axis alignment (along with excellent bias repeatability in the accelerometer) to greatly simplify the frame alignment process.
LINEAR, TEMPERATURE-CONTROLLED BEHAVIOR. Temperature gradients on tractors and implements are large. ADI incorporates temperature compensation models enforced at both the sensor and system level. The goal is not perfect thermal invariance, which is unrealistic in cost-sensitive segments, but predictable behavior that fusion algorithms can model accurately.
FUSION-FIRST PHILOSOPHY. ADI treats the IMU as one component of a larger PNT solution. Their systems are designed for tight integration with GNSS receivers, wheel speed sensors, magnetometers, and vehicle CAN data. Robust synchronization and time-based alignment of the inertial output simplifies system coupling. This architecture enables robust attitude estimation and velocity smoothing, especially during headlands or canopy exposure.
PREDICTABLE LIFECYCLE PERFORMANCE. Agricultural platforms must last. ADI designs for multi-season reliability and bounded long-term drift. The objective is to ensure a machine equipped with an ADI IMU behaves the same in year four as it did in year one.
“You can’t calibrate a sensor’s inherent noise performance, its stability, or its response to vibration,” Galchev said. “These unmodeled error sources directly produce error at the output, and that’s where ADI focuses on innovating at the chip level.”
This technical discipline supports the system-level view: Inertial is not a premium feature; it is a foundation for reliable GNSS-enabled autonomy.
Integration in the Field: What Engineers Face
Engineers integrating inertial systems into agricultural machines confront real-world constraints that differ from lab conditions. ADI’s field experience highlights specific patterns.
Booms flex. Toolbars vibrate. Tractor frames twist. Sensor placement often becomes a compromise. An INS may be exposed to off-axis motion uncorrelated with actual vehicle trajectory. ADI mitigates this through calibration routines, filtering strategies, and noise modeling that treat flex and vibration as signal partitions.
Implements as Independent Dynamic Systems
The implement behind a tractor behaves differently from the tractor itself. For operations like variable-rate spraying or multi-row harvesting, implement attitude, even when decoupled from tractor motion, must be sensed accurately. IMUs can be mounted on booms or frames to track these dynamics.
Agricultural systems rely on multiple data streams: GNSS, wheel speed, steering angle, hydraulic cylinder positions, and sometimes LiDAR or camera inputs. INS integration requires precise timing alignment. ADI designs its systems for deterministic latency and reliable time stamping, which improves fusion accuracy.
The transition from row guidance to headland turns stresses both GNSS and INS. Machines accelerate, decelerate, rotate sharply, and pass through GNSS-obstructed corners. ADI’s inertial fusion helps maintain attitude and velocity states during these high-dynamic transitions.
Agricultural drones operate close to trees and terrain. Ground robots operate beneath canopy. INS solutions provide roll/pitch stability, altitude smoothing, and fallback motion propagation when GNSS is degraded.
Economics: Performance Within Reach
Precision agriculture is expanding beyond large, capital-intensive farms. The next wave of adoption will come from mid-market operations and mixed-crop geographies.
• Cost matters. Expensive IMUs are non-starters. ADI designs MEMS-based solutions that offer robust performance within an accessible cost envelope.
• Scalability drives OEM decisions. Manufacturers want sensors available in volume, with predictable lead times and long lifecycle commitments.
• Global adoption requires price/performance balancing. Emerging markets need PNT reliability but cannot bear aerospace-grade costs. Scalable, rugged MEMS solutions fill this gap.
• Autonomy ROI depends on continuity. If a machine can maintain guidance through GNSS disruptions, it can operate longer hours and at higher speeds, improving economics for both OEMs and end-users.
“Just because you go from a big tractor to a smaller tractor, the conditions don’t change that much,” Galchev said. “If you want to achieve the same mission profile, you still need the same performance level.”
As ADI brings cost-efficient inertial capability into mainstream ag equipment, the performance gap between high-end and mid-tier platforms narrows.
The Road Ahead: Multi-Sensor Fusion and Autonomy
Agriculture is evolving toward heterogeneous fleets: autonomous tractors, robotic harvesters, terrain-following sprayers, orchard drones, and edge-connected implements. All require resilient PNT.
End-of-row autonomy
Low-speed, high-precision maneuvers demand stable attitude estimation. INS ensures smooth transitions even in partial GNSS shadows.
Terrain-following and boom dynamics
Sprayers rely on roll/pitch estimates for boom control. IMU data supports rapid damping of boom oscillation, improving chemical placement, reducing drift, and lowering input costs.
Cooperative ground-air systems
Drones performing scouting missions must integrate with guidance systems on the ground. Consistent inertial performance across platforms enables better data fusion and farm-level coordination.
Resilience as a design requirement
Interference, accidental or intentional, is increasingly common. INS helps maintain continuity of operation when GNSS performance degrades. It stabilizes machine behavior during uncertainty and helps diagnostic systems detect anomalies.
Regulatory evolution
As autonomy expands, functional-safety requirements will increase. INS adds a measurable layer of redundancy and validation, supporting safety cases for next-generation machines.
“We have sensors that we released more than 20 years ago still being produced,” Galchev said, “because our customers’ systems have long lifespans and once something works, it can be very difficult and expensive to re-qualify and swap it out.”
As autonomy accelerates, the next decade of agriculture will be shaped by platforms that assume GNSS variability and engineer around it from day one. That shift elevates inertial from an add-on to a core requirement. ADI, with its long record of sensor innovation and system-level discipline, is positioned to anchor that transition. Their approach: predictable drift behavior, calibration at the silicon level, ruggedized packaging, and tight GNSS-INS fusion, gives OEMs a stable foundation to build autonomy across tractors, implements, drones, and emerging agricultural robots. The path forward is clear: Resilient PNT will define productivity, and ADI’s inertial technology will increasingly sit at the center of the autonomy stack, enabling machines that navigate, adapt and operate with confidence in the real conditions of the farm.
