hirooshaweerasuriya/rice-leaf-disease-mobilenetv2

Rice Leaf Disease Detection (MobileNetV2)

Fine-tuned MobileNetV2 that classifies rice (paddy) leaf images into 8 categories. Validation accuracy: 76.04%.

Classes

0. Bacterial Leaf Blight
1. Brown Spot
2. Healthy Rice Leaf
3. Leaf Blast
4. Leaf scald
5. Narrow Brown Leaf Spot
6. Rice Hispa
7. Sheath Blight

Label order matters — index i of the softmax output corresponds to item i above.

Usage

import numpy as np, json
from huggingface_hub import hf_hub_download
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input

repo = "hirooshaweerasuriya/rice-leaf-disease-mobilenetv2"
model  = load_model(hf_hub_download(repo, "rice_disease_model.keras"))
config = json.load(open(hf_hub_download(repo, "config.json")))
labels = [config["id2label"][str(i)] for i in range(config["num_classes"])]

img = image.load_img("leaf.jpg", target_size=(224, 224))
x   = preprocess_input(np.expand_dims(image.img_to_array(img), 0))

probs = model.predict(x)[0]
print(labels[int(probs.argmax())], f"{probs.max():.1%}")

Preprocessing must match training: preprocess_input scales pixels to [-1, 1]. Using /255.0 instead will produce confident, wrong answers.

Training

• Base: MobileNetV2, ImageNet weights, include_top=False
• Head: GlobalAveragePooling2D → Dropout(0.3) → Dense(128, relu) → Dropout(0.2) → Dense(8, softmax)
• Stage 1: backbone frozen, Adam lr=1e-3, 8 epochs
• Stage 2: top 30 layers unfrozen (BatchNorm kept frozen), Adam lr=1e-5, 6 epochs
• Augmentation: rotation 25°, zoom 0.2, shifts 0.1, shear 0.1, horizontal flip, brightness 0.8–1.2
• Class imbalance handled with balanced class_weight
• Data: anshulm257/rice-disease-dataset, 80/20 split
• Trained on a Kaggle free-tier GPU notebook

Results

Metric	Value
Validation accuracy	0.7604
Validation loss	0.7156

Class	Val samples	Recall
Bacterial Leaf Blight	107	80.37%
Brown Spot	162	61.11%
Healthy Rice Leaf	102	91.18%
Leaf Blast	185	62.16%
Leaf scald	112	70.54%
Narrow Brown Leaf Spot	70	64.29%
Rice Hispa	132	87.88%
Sheath Blight	165	93.33%

Limitations

• Trained on a single curated dataset; field photos with varied lighting, backgrounds and phone cameras will be harder.
• The dataset appears pre-augmented, so near-duplicates may span the train/val split — real-world accuracy is likely below the number above.
• Always returns one of the 8 classes; a photo of something else still gets a confident label. Threshold on max probability if that matters.
• A decision-support tool, not a substitute for agronomist diagnosis.

Files

File	Description
rice_disease_model.keras	Keras 3 model
saved_model/	TensorFlow SavedModel
model.tflite	Quantised TFLite build for mobile
config.json	Labels, image size, preprocessing, metrics
labels.txt	Class names in index order