Okuafo MaizeGuard Edge v1.4
Okuafo MaizeGuard Edge v1.4 is a compact, two-stage TensorFlow Lite system for
offline maize-leaf screening. It first screens whether an image is diseased,
healthy, or not suitable for maize screening. When a disease signal passes the
configured confidence gate, a second model screens the leaf region for five
disease-pattern categories.
Evidence statement: Research-preview maize screening model evaluated on
documented development datasets and limited field samples.
This is not a claim of independent field validation, agronomic diagnosis,
production readiness, release approval, or license clearance. The existing
repository is publicly visible, but visibility is not permission to redistribute,
deploy, or create derivative works. Every output requires qualified human review
before treatment, pesticide, financial, or other consequential decisions.
Model files
| File | Role | Output classes | Size |
|---|
models/okuafo_maize_stage1_v1_4_fp16.tflite | maize/relevance and health gate | diseased, healthy, not_maize_or_unclear | 24.5 MiB |
models/okuafo_maize_stage2_disease_roi_v1_4_fp16.tflite | disease-pattern screening after a positive gate | bacterial_leaf_streak, common_rust, gray_leaf_spot, northern_leaf_blight, other_disease | 24.5 MiB |
Both models use an EfficientNetV2B3 backbone, a 384 × 384 × 3 RGB input,
FP16-compressed weights, and raw RGB values in the 0–255 range. The input and
output tensors remain float32.
Intended use
- Research review and authorized evaluation of offline, human-reviewed maize
leaf screening on compatible mobile or edge hardware.
- Triage research for field-scouting and agronomy workflows with a qualified
human reviewer.
- Research on small, deployable agricultural vision systems.
- A baseline for future models trained and evaluated with geographically and
seasonally representative field data.
Out-of-scope use
- Autonomous diagnosis or treatment selection.
- Pesticide, fertilizer, insurance, credit, or yield decisions without expert
review and independently verified evidence.
- Diagnosis from images that do not clearly show a maize leaf.
- Use as a general-purpose crop, livestock, or environmental classifier.
- Redistribution, deployment, or derivative use while LICENSE
provides no license grant.
Quick start
The following command is for authorized local review of this staging package:
1python -m venv .venv
2source .venv/bin/activate
3pip install -r requirements.txt
4python inference/predict.py path/to/maize_leaf.jpg
The command prints structured JSON containing the gate decision, class
probabilities, whether Stage 2 ran, and the final screening result. See
inference/README.md for the exact integration boundary.
Local package validation
Run the fail-closed, standard-library package validator and the split-tool unit
tests before sharing an authorized review copy:
1python scripts/validate_release.py
2python -m unittest discover -s eval -p 'test_*.py' -v
These checks verify claims/status gates, audit and repair-plan invariants,
artifact hashes, metadata posture, SVG safety text, and rendered-card structure.
They do not substitute for corrected-split retraining, model evaluation, license
clearance, or independent field validation.
Evaluation evidence and split warning
Research-preview maize screening model evaluated on documented development
datasets and limited field samples.
A retrospective audit of the original v1.4
metadata.csv found
80,313
records,
68,786 source + group IDs, and
2,205 groups crossing the
train/validation/test partitions. The recorded split is therefore not a
leakage-safe independent evaluation, and its historical metrics must not be used
as public performance or field-results claims. The machine-readable findings
are in
eval/development-data-audit.json, and
the reproducible audit is in
eval/audit_split_manifest.py.
The deterministic repair tool generated a group-disjoint assignment plan for
all
80,313 records:
57,914 train,
11,107 validation, and
11,292
test, with
zero cross-split groups and
zero cross-split records. See
eval/rebuild_grouped_manifest.py and
eval/corrected-split-plan.json. This plan fixes
the assignment design only: the packaged models have
not been retrained or
evaluated on it, and an independent Ghana field holdout is
not complete.
For traceability only, the packaged metadata retains these legacy development
metrics:
| Stage | Legacy development metric | Historical value |
|---|
| Stage 1 triage gate | accuracy | 0.9920846819877625 |
| Stage 2 disease-pattern screen | accuracy | 0.9934966564178467 |
These values are not release metrics or field-accuracy estimates. A
fresh retraining run and corrected evaluation using the group-disjoint plan
remain open release gates. Limited field samples were used only to exercise the
end-to-end routing path; the recorded diseased sample was not independently
labelled by an agronomist. See
eval/SMOKE_TESTS.md.
Training-data record
No source images are distributed with this model repository. Local training
records cite PlantVillage, PlantDoc, Ghana CCMT, seasonal maize-disease imagery,
a multi-crop maize-disease archive, OSF northern-leaf-blight imagery, and MIT
Indoor Scenes for open-set rejection.
The source trail, acknowledgements, and unresolved license questions are recorded
in
CITATIONS.md. That record does not itself grant permission to
redistribute the weights.
Limitations and risks
- Disease symptoms can overlap with nutrient stress, chemical injury, weather
damage, pests, senescence, and mixed infections.
- The model does not estimate causal disease severity, recommend treatments, or
measure chlorophyll/SPAD.
- Image blur, glare, shadows, occlusion, unusual cultivars, early symptoms, and
background clutter may cause rejection or incorrect classification.
other_disease means the image appears abnormal but does not support a named
class; it is not a diagnosis.- Performance may vary substantially by camera, farm, region, season, cultivar,
and crop stage; those variations have not been independently characterized.
Responsible evaluation
Present every output as a screening result with confidence and an explicit
uncertainty state. Preserve the not_maize_or_unclear and other_disease
pathways. Ask the user to retake poor images, route consequential cases to a
qualified human, and log the model version and thresholds so errors can be
audited.
Release and license status
This directory mirrors a publicly visible research-preview repository. Public
visibility describes access only; it does not mean the model has public-release
or production approval. The package includes two model artifacts, metadata,
reference inference code, checksums, source citations, safety guidance, and
editable announcement artwork.
License review is pending and
LICENSE provides no license grant. This
update does not change repository visibility. Do not redistribute or deploy the
model artifacts until every item in
RELEASE_CHECKLIST.md is resolved and an explicit
compatible model license is approved. Any decision to restrict repository access
requires separate release-owner and legal authorization.
Citation
1@software{sinuosity_okuafo_maizeguard_edge_2026,
2 author = {{Sinuosity AI}},
3 title = {Okuafo MaizeGuard Edge v1.4},
4 year = {2026},
5 version = {1.4},
6 note = {Research-preview two-stage TensorFlow Lite maize screening model; independent field validation and license review pending}
7}
Maintainer
Sinuosity AI — physical intelligence research for systems that work beyond the lab.
