ixrbhii/guava-disease-convnext

Guava leaf disease classifier

Tells which of 6 classes a guava leaf photo shows: Healthy, Rust / Leaf Spot, Tea Mosquito Bug / Thrips, Anthracnose, Multiple Diseases, YLD.

ConvNeXt-Small (convnext_small, from convnext_small.fb_in22k_ft_in1k), 224 × 224 RGB, 99 MB (half precision). 100.0 % test accuracy, macro-F1 1.000, on 1,345 held-out photos. Test photos come from the same datasets the model learned from, so real field photos score lower.

YLD is the dataset's own label, kept as is.

Class	Recall	F1	Test photos
Healthy	100.0%	1.000	510
Rust / Leaf Spot	100.0%	1.000	125
Tea Mosquito Bug / Thrips	100.0%	1.000	125
Anthracnose	100.0%	1.000	335
Multiple Diseases	100.0%	1.000	125
YLD	100.0%	1.000	125

Use

import timm, torch
from PIL import Image

model = timm.create_model("hf-hub:ixrbhii/guava-disease-convnext", pretrained=True).eval()
cfg = timm.data.resolve_data_config({}, model=model)
x = timm.data.create_transform(**cfg)(Image.open("leaf.jpg").convert("RGB")).unsqueeze(0)
p = model(x).softmax(-1)[0]
labels = model.pretrained_cfg["label_names"]
for i in p.argsort(descending=True)[:3]:
    print(labels[i], f"{100 * p[i]:.1f} %")

The training images were resized to 224 × 224 without cropping; timm's transform above (resize + 224 centre crop) gives the same result for square photos. label_descriptions in config.json maps each name to the dataset's original label. Best results: one leaf filling most of the photo, in daylight and in focus. Below about 45 % confidence, ask for a better photo.

In an app: LULC Fetch (Agri ▸ Diagnose crop disease) recognises the crop first and then runs the right disease model, with a photo-quality check and a disease map from geotagged photos.

Related

• All 44 models (2 crop detectors, 42 crops) as separate repositories: the collection
• All 44 models in one repository: ixrbhii/multicrop-disease-models
• Symptoms, treatment and pests for each disease: ixrbhii/crop-disease-qa

Training data and licence

Trained on public leaf-disease photo datasets, including PlantVillage (CC0) and MangoLeafBD (CC-BY-4.0). Released under CC-BY-4.0: please credit "Multi-crop disease models, IXR (agritechixr)" and the training datasets. A diagnosis supports, but doesn't replace, a local agriculture expert.