# Prospective study protocol

## Cross-sensor optical screening of rice bacterial leaf blight suspect areas with DJI Mavic 3M

**Protocol date:** 2026-08-04  
**Study status:** protocol and engineering demonstration; no Taiwan field result is claimed  
**Primary task:** map areas whose optical pattern is consistent with the training definition of rice bacterial leaf blight (BLB) and flag them for human inspection  
**Not a study endpoint:** pathogen confirmation, legal compliance, food safety, yield guarantee or autonomous pesticide application

## 1. Research questions

1. Can an openly licensed Phantom 4 Multispectral BLB segmentation corpus support a reproducible common-band baseline?
2. How much performance is lost when the model is transferred to calibrated Mavic 3M imagery from a different field, date, cultivar or disease prevalence?
3. Does local fine-tuning with independently annotated Mavic 3M plots improve disease-suspect mapping without concealing the cross-domain baseline?
4. Can uncertainty-based abstention reduce unsafe high-confidence errors at a useful field-coverage level?

The study evaluates optical **screening**, not biological diagnosis. BLB must ultimately be confirmed through qualified field assessment and, where required, laboratory testing.

## 2. Evidence basis

- The open BLB corpus and its method article are registered at <https://doi.org/10.6084/m9.figshare.26955862> and <https://doi.org/10.1371/journal.pone.0314535>.
- A separate Mavic 3M BLB study, <https://doi.org/10.3390/rs18060880>, reported materially lower overall accuracy in an independent field/year scenario than in a same-distribution test. Its data are not openly downloadable and its reported results are contextual evidence, not training observations for this project.
- DJI's official Mavic 3M documentation identifies four 5 MP multispectral channels centered at Green 560 ± 16 nm, Red 650 ± 16 nm, Red-edge 730 ± 16 nm and NIR 860 ± 26 nm. DJI also states that image values are DN rather than reflectance. Radiometric correction and a calibration panel are therefore part of the protocol.

## 3. Design overview

### Phase A — reproducibility baseline

1. Verify the BLB Figshare DOI/version, CC BY 4.0 notice, file count and checksums.
2. Reproduce a within-record baseline using only common deployment channels: G, R, RE and NIR.
3. Recompute indices rather than trusting undocumented cached values:
   - `NDVI = (NIR - R) / (NIR + R)`
   - `NDRE = (NIR - RE) / (NIR + RE)`
   - `GNDVI = (NIR - G) / (NIR + G)`
4. Treat the released 70/15/15 split as a reproduction split only because field/date groups are unavailable and patch overlap creates a possible spatial-leakage pathway.

### Phase B — Mavic 3M engineering transfer

1. Ingest calibrated Mavic 3M single-band TIFFs while preserving geotransform, CRS, nodata and capture metadata.
2. Co-register bands and reject tiles that exceed a predeclared alignment tolerance.
3. Apply the same common-band scaling learned only from training data.
4. Run the frozen Phase A model to establish an unadapted cross-sensor baseline.
5. Fine-tune only on properly authorized and independently labeled Mavic 3M training fields. A target field/date designated as test data remains untouched until final evaluation.

The GD-RGBMS-DB archive is not authorized for this phase unless its currently blank License field is replaced or supplemented by explicit written permission covering training and resulting weights.

### Phase C — prospective Taiwan validation

No field has been sampled under this protocol yet. Before collection, obtain landholder/operator permission and any required aviation authorization. Use complete-field or plot identifiers that can be anonymized in public outputs.

Suggested acquisition controls, to be finalized with the flight operator and plant pathologist:

- collect reflectance-panel images immediately before and after a flight without shade on the panel;
- keep the Mavic 3M sunlight sensor unobstructed;
- prefer stable illumination with solar elevation above 30 degrees, following DJI guidance;
- keep altitude, speed, view angle, overlap, exposure, calibration and processing version constant within a comparison series;
- record rainfall, irrigation, growth stage, cultivar, planting density and known nutrient or lodging conditions;
- acquire RTK/ground-control information sufficient to revisit validation plots;
- collect healthy, visually ambiguous and suspected-disease samples rather than sampling only obvious symptoms.

Ground labels require a documented rubric and qualified plant-pathology review. Laboratory confirmation is recorded separately where available. A model label remains “BLB suspect” when confirmation is absent.

## 4. Analysis units and split policy

- **Primary independent unit:** field or physically separated plot within a flight date, not a pixel or overlapping patch.
- **Grouping keys:** site, field, flight date, orthomosaic, cultivar and season.
- Assign entire groups to train, validation or test **before** extracting 256 × 256 or other patches.
- No neighboring or overlapping patch from a test group may appear in training or validation.
- Normalization statistics, augmentation policy, class weights, threshold selection and calibration are fitted on training/validation data only.
- An external test consists of at least one complete field/date/cultivar combination absent from training. Same-field and external results are reported separately.
- If the sample supports it, use leave-one-field-out or leave-one-date-out evaluation. Do not replace a missing independent field with a random pixel split.

## 5. Models

### Interpretable baseline

A Random Forest or regularized tree model uses calibrated G/R/RE/NIR reflectance, NDVI, NDRE and GNDVI summarized at pixel, superpixel or plot level. Feature importance is descriptive and must not be treated as causal biology.

### Segmentation baseline

A compact U-Net with ResNet-34 encoder, or equivalently bounded architecture selected before viewing the external test, predicts:

1. healthy rice;
2. low-severity BLB-suspect pattern;
3. high-severity BLB-suspect pattern;
4. other/non-rice/background.

Use class-weighted cross-entropy plus Dice, or a predeclared focal-plus-Dice loss. Large architecture searches are avoided until the grouped baseline is established. Optional transfer architectures must be compared against the same split and preprocessing.

### Uncertainty and abstention

Calibrate class probabilities on validation groups using a declared calibration method. Pixels/plots above an uncertainty threshold are labeled “inspection required” rather than forced into a disease class. Thresholds are chosen against an agronomically defined false-negative/inspection-cost tradeoff before opening the external test.

## 6. Outcomes and statistical reporting

Report all of the following rather than overall accuracy alone:

- per-class IoU and macro mIoU;
- disease-suspect Dice/F1, precision, recall and confusion matrix;
- PR-AUC for imbalanced disease classes;
- false-negative rate and false-positive alerts per hectare;
- plot-level affected-area MAE/RMSE;
- weighted Cohen's kappa for healthy/low/high ordinal severity;
- Brier score or expected calibration error and coverage-risk under abstention;
- inference time, tile throughput and peak memory for the stated hardware;
- same-distribution versus cross-field/year/cultivar performance difference.

Confidence intervals are bootstrapped or clustered at field/plot level. Pixels are not treated as thousands of independent biological replicates. Report every predeclared external test result, including negative or inconclusive findings.

## 7. Domain-shift and confounding controls

Expected shifts include sensor center wavelength, spectral response, calibration, ground sampling distance, altitude, overlap, co-registration, shadows, cloud and exposure; cultivar, stage, nitrogen, irrigation, planting density and background; geography, pathogen strain, prevalence, natural versus managed infection and annotation practice.

Low vegetation-index response is nonspecific. Nutrient deficiency, drought, flooding, pests, weeds, soil/water background, lodging, senescence and shadow can resemble disease stress. Hyperspectral blast indices using narrow 724/736/816 nm wavelengths are not implemented with the broadband Mavic 3M RE/NIR channels.

## 8. Reproducibility and stopping rules

- Lock dataset DOI/version, file manifest, checksum, environment and random seeds before training.
- Save every split manifest and model configuration; never select a “best” test seed.
- Unit-test band order, index formulas, nodata masking, spatial transforms and mask alignment.
- Stop and investigate if band registration, metadata, label provenance or rights cannot be verified.
- Do not publish a deployment model if only random overlapping-patch validation is available.
- Do not output automatic spray commands. A future treatment module requires a separate safety, efficacy, legal and human-authorization study.

## 9. Reporting language

Permitted: “The model flags optical patterns consistent with the training definition of BLB and requiring field inspection.”

Not permitted: “The drone diagnoses BLB,” “the plant is infected,” “the field is safe,” “the model proves pesticide treatment is required,” or any Taiwan accuracy claim before a genuine independent Taiwan test is completed.
