Sensor & machinery data quality
Which measurements are reliable enough to use?
Review calibration evidence, source units, recording gaps, GNSS alignment and quality flags.
PROJECT OUTPUT
Assess this work ↗Quality assessment + measurement ledger
FIELD SCIENCE / SPATIAL INTELLIGENCE
Reliable data.
Clearer agricultural
decisions.
Understand what your measurements can support. Agricultural science, sensor data and GIS brought together in traceable analysis for research, monitoring and field decisions.
SENSORS / GIS / TRIAL SCIENCE / DATA SYSTEMS
FIELD OBSERVATIONSHENRYS / MT, USA
N ↑
HARVEST / 2018Loading observations…Recorded dry yield · bu/ac
01 / OPEN FIELD STUDYLow → high recorded yieldSource & methods ↗
Published machine data · OFPEDATA / Montana State University
Historical observations. This is not a live sensor feed.
TECHNICAL ECOSYSTEM
Open tools. Portable outputs.
Python
R
QGIS
PostgreSQL
Git
GDALSelected to suit each project. Demonstration uses Python and browser-native GIS.
52,827original yield records
2historical harvest datasets
563recorded protein samples
400spatial bootstrap resamples
Verified counts from the open-data demonstration. Inspect the evidence ↗
01 / SERVICES
Scientific depth.
Practical outputs.
Support for agronomy teams, crop and input R&D, research networks and agtech developers. Scope the work around a decision and the evidence available.
Agricultural GIS
What changes across fields, sites and seasons?
Align soil samples, terrain, management records and field observations in a documented spatial framework.
PROJECT OUTPUT
Assess this work ↗GIS layers + spatial diagnostics
Remote sensing & crop monitoring
What can imagery tell us, and at what scale?
Compare dated imagery with field observations. Check resolution, coverage and seasonal context before interpreting change.
PROJECT OUTPUT
Assess this work ↗Monitoring maps + validation plan
Field trial design & analysis
Does the experiment support the decision?
Specify replication, randomization and experimental units. Analyse crop and input responses with design-appropriate methods.
PROJECT OUTPUT
Assess this work ↗Trial protocol + research report
Agricultural data integration
Can your team follow a result back to its source?
Connect sensor, machinery, laboratory and GIS records with explicit identities, quality checks and repeatable processing.
PROJECT OUTPUT
Assess this work ↗Data pipeline + technical handover
Scientific reporting & analytical tools
Can people inspect and use the findings?
Build clear reports and tailored workspaces that expose assumptions, uncertainty, screening choices and source evidence.
PROJECT OUTPUT
Assess this work ↗Technical report + analytical workspace
02 / EXPLORE THE PRACTICE
Start with the question.
Build the evidence.
Reliable measurements. Spatial understanding. Better experiments. Connected systems. Each starts with a distinct agricultural question and a clear analytical purpose.
Know which measurements
you can trust.
Investigate sensor drift, gaps and disagreement between instruments. Soil-moisture networks, weather stations and machinery logs each need a quality review grounded in their measurement setup.
- Instrument metadata, calibration records and source units
- Time-series gaps, outliers and cross-sensor consistency
- GNSS alignment and machine-record quality
- Documented quality flags and monitoring workflows
PROJECT OUTPUT
A sensor-quality assessment, measurement ledger and monitoring specification. Corrections require supporting instrument evidence.
BUILT AROUND YOUR WORK
Agronomy teams
Crop & input R&D
Research & trial networks
Agtech developers
03 / HOW THE WORK PROCEEDS
A clear question.
A traceable answer.
Agree what the evidence needs to show, make the analytical choices explicit, and deliver results your team can review.
Assess the evidence
Review the agricultural decision, measurement setup, available files and coverage. Identify gaps before choosing the analysis.
QUESTION + DATA INVENTORYScope and validate
Agree the method, outputs and review points. Check units, experimental design, spatial structure and sensitivity to assumptions.
ANALYTICAL PLAN + VALIDATIONDeliver and hand over
Present findings with uncertainty and limitations. Supply documented outputs, source references and reproducible methods.
FINDINGS + METHODS + HANDOVER04 / INSPECT THE WORK
Evidence you can examine.
Two analytical examples from one published wheat-field study. Inspect the files, change the screening rules and review what the results support.
EXAMPLE 01HENRYS / 2018
50.8%of retained yield records matched
PUBLIC DATA / EXPLORATORY ANALYSISAGRICULTURAL DATA / SPATIAL EVIDENCE
Point matching exposes a coverage gap.
- Challenge
- Application locations and harvest observations were recorded separately. Proximity alone does not guarantee useful coverage.
- Approach
- Screen yield records and compare spatial matching radii. Use the balanced profile and a 12 m matching radius for this example.
- Observed result
- 30,133 yield records retained; 15,302 matched to application points. The remaining 14,831 stay visible as unmatched.
EXAMPLE 02HENRYS / 2016
88.7%of retained yield records matched
PUBLIC DATA / EXPLORATORY ANALYSISAGRICULTURAL DATA / SPATIAL EVIDENCE
Polygon matching changes the analytical coverage.
- Challenge
- A second harvest uses application polygons rather than points. It needs a different spatial matching method.
- Approach
- Apply documented screening and polygon-based matching. Preserve the source geometry and the limitations of the available machine attributes.
- Observed result
- 19,970 yield records retained; 17,709 matched. Speed and recording time were not supplied in this harvest batch.
Independent exploratory analysis of OFPEDATA, © 2020 Montana State University, MIT license. Historical public data; not commissioned client work. Counts use the balanced screening profile. Associations do not establish causal treatment effects. Source manifest ↗
05 / SCIENTIFIC STANDARD
The confidence
comes from
the method.
A clear map is useful. A defensible interpretation requires agricultural context, measurement discipline, and statistical judgement.
Inspect our demonstration methods ↗Agronomy before algorithms.
Crop, treatment, management history and trial design belong in the analytical specification. A field is more than a table of coordinates.
Measurements with provenance.
Preserve source files, instrument metadata, calibration records, units and quality decisions. Never silently substitute an assumption for a measurement.
Uncertainty in the open.
Report effect size, intervals, sensitivity and validation. State whether the design supports a causal conclusion or an observational association.
Work you can reproduce.
Deliver documented code, data dictionaries, processing ledgers and model specifications alongside the report.
06 / ENGAGEMENTS
One field question.
Or an entire research programme.
Data assessment
Review the agricultural question, measurement setup and existing data. Identify the evidence and technical work needed.
OUTPUT / scoped analytical planDefined project
Agree a defined scope, fee and review points for sensor assessment, spatial analysis, research reporting or analytical software.
OUTPUT / evidence & reproducible methodsOngoing programme support
Connect monitoring, research and geospatial data through maintainable pipelines and tailored analytical tools.
OUTPUT / maintained analytical workflow07 / COMMON QUESTIONS
Before you begin.
Scope, evidence and ownership should be clear from the start.
Prepare your assessment brief ↗Can you work with messy or incomplete data?
An assessment starts with the files as they are. Review source units, coverage, recording gaps and measurement context first. Some gaps can be managed; others limit which conclusions are defensible.
Does a mapped association prove a treatment benefit?
No. A causal treatment conclusion depends on the experimental design, including randomization and independent replication. Historical machine records can reveal patterns and generate hypotheses; they do not establish causality on their own.
Will the outputs fit our existing GIS and software?
Agree file formats, coordinate systems, data ownership and integration requirements during scoping. GIS layers, documented datasets, reports and reproducible code can be specified around your workflow.
What happens to our data and the analytical methods?
Set access, retention and ownership in the project agreement. Define which source files, code, data dictionaries and quality ledgers are included in the handover. This preview uploads no files and sends no assessment requests.
START WITH YOUR QUESTION
Begin with a data assessment.
Define the question, review the available evidence, and identify a practical route to the result you need.