Marconcini/leafscan-convnext-tiny

LeafScan — ConvNeXt-T for plant disease classification (field-calibrated)

Classifies a leaf photo into 38 crop / disease classes (14 crops) and says "Not sure" when its confidence is too low. Research / portfolio project: github.com/marconcini/leafscan — every number below comes from experiments documented there, with hypotheses written before results.

• Architecture: ConvNeXt-T (timm: convnext_tiny.fb_in22k_ft_in1k), 28M parameters, 224×224 input.
• Training: ImageNet weights → PlantVillage (lab photos, 39k images) → 85% of PlantDoc train (1,975 web/field photos). The other 15% of PlantDoc train (348 photos) is used only to fit a temperature (T = 0.83) and an abstention threshold (0.75 on calibrated confidence).

Results

	PlantDoc test (field, 228 photos)	PlantVillage test (lab, 10,709 photos)
Accuracy	72.4% (95% CI 66.2–78.1)	96.6%
Right crop	86.4%	–
Healthy vs diseased	94.7%	–
Answers above the threshold	65% of photos, 87% of them right	94% of photos, 98.7% right

For context: the same architecture trained on PlantVillage only reaches 99.7% on lab photos but 31% on field photos. Lab accuracy says little about real-world use.

How to use

pip install "leafscan @ git+https://github.com/marconcini/leafscan.git"

from PIL import Image
from leafscan.predictor import Predictor

predictor = Predictor("Marconcini/leafscan-convnext-tiny")   # downloads config.json + model.safetensors
pred = predictor(Image.open("leaf.jpg"))
print(pred.verdict)   # diagnosis, or "Not sure" below the field-calibrated threshold
print(pred.top)       # top-3 (class, calibrated probability)

Files:

• model.safetensors — PyTorch weights; config.json — class names, input size, temperature, threshold (and those of the int8 model under onnx_int8).
• model.onnx — fp32 ONNX export, identical top-1 to PyTorch on PlantDoc test.
• model_int8.onnx — 32 MB, linear layers quantised to int8 (convolutions kept fp32, as ONNX Runtime Web has no int8 convolution). Used by the in-browser demo. Same predictions as fp32 on 96.5% of PlantDoc test photos; 71.9% accuracy. Because quantisation changes some predictions, it has its own temperature and threshold, refitted on the same field calibration set: on PlantDoc test it answers 66% of photos, 84.7% of them right.

In the browser (ONNX Runtime Web, image resizing done by the canvas instead of PIL), the demo agrees with the Python int8 predictions on 96.1% of PlantDoc test photos.

Intended use and limitations

• Intended use: demonstration and research on robustness of plant-disease classifiers. Not a diagnostic tool and not a substitute for an agronomist.
• Knows only the 38 PlantVillage classes; any other plant or disease is forced into one of them (or answered "Not sure").
• Test set is small (228 photos): differences under ~6 points are within noise.
• Calibration and threshold were fitted on PlantDoc photos; they may not hold for photos from other sources (the project shows lab-fitted thresholds failing on field photos).
• Rejection of non-leaf images is weak next to real field photos (AUROC 0.6–0.8 in the project's tests): a photo of something else may still receive a diagnosis.
• PlantDoc is web-scraped and contains label noise (duplicates filed under different classes were removed).

Training data and licenses

• PlantVillage (Hughes & Salathé, 2015), CC BY-SA 3.0 — via mohanty/PlantVillage.
• PlantDoc (Singh et al., 2020), CC BY 4.0 — github.com/pratikkayal/PlantDoc-Dataset.
• Weights released under CC BY-SA 4.0 (share-alike, following PlantVillage's license). Code: MIT.

References

• Hughes & Salathé (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv:1511.08060
• Singh et al. (2020). PlantDoc: A Dataset for Visual Plant Disease Detection. CoDS-COMAD
• Liu et al. (2022). A ConvNet for the 2020s. CVPR
• Guo et al. (2017). On Calibration of Modern Neural Networks. ICML