plant-disease-efficientnetv2s

🌿 Plant Disease Detection — EfficientNetV2S

Automated plant disease classification trained on the PlantVillage benchmark (38 classes, 54,306 images).

Live demo: 🤗 HuggingFace Space
GitHub: Animesh-Kr/Plant-Disease-Prediction

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Model Details

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Evaluation Results

Interactive Visualisations

Statistical Significance

• McNemar's test: p = 3.27 × 10⁻¹⁸² — improvement is astronomically significant
• Expected Calibration Error (ECE): 0.0902 — moderately well calibrated
• MC Dropout uncertainty (30 passes): flagged images are ~17 percentage points less accurate than unflagged, confirming the uncertainty flag is meaningful

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Training Pipeline

Data

• Dataset: PlantVillage color subset — 54,306 images, 38 disease classes
• Split: Family-aware 70/15/15 train/val/test using perceptual hash deduplication
• Deduplication: pHash + Union-Find clustering prevents near-duplicate leakage
• Augmentation: RandomFlip, RandomRotation(0.08), RandomZoom(0.12), RandomTranslation(0.08), RandomContrast(0.10)

Architecture

Input (384×384×3)
    ↓
Augmentation layers
    ↓
EfficientNetV2S backbone (ImageNet pretrained, include_preprocessing=True)
    ↓  ← top 40% unfrozen during fine-tune stage
GlobalAveragePooling2D
    ↓
Dropout(0.3) → Dense(512, swish) → Dropout(0.3)
    ↓
Dense(38, softmax)

Training Strategy

• Stage 1 (Warmup): Backbone frozen, head only, lr=2e-3, 8 epochs
• Stage 2 (Fine-tune): Top 40% backbone unfrozen, lr=2e-5, 15 epochs
• Loss: CategoricalCrossEntropy + label smoothing 0.1
• Optimizer: AdamW (weight decay 1e-4)
• Precision: Mixed float16
• Batch size: 64 @ 384×384 (A100 80 GB)
• Hardware: Google Colab Pro+ A100 80 GB, 167 GB RAM

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Supported Classes (38 total)

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Usage

Option A — TFLite float16 (recommended, fast)

import tensorflow as tf
import numpy as np
import json
from PIL import Image

# Load model
interp = tf.lite.Interpreter(model_path="model_float16_quant.tflite")
interp.allocate_tensors()
ind  = interp.get_input_details()[0]
outd = interp.get_output_details()[0]

# Load labels
with open("class_indices.json") as f:
    class_map = json.load(f)   # {"0": "Apple___Apple_scab", ...}

def predict(img_path: str, top_k: int = 5):
    img = Image.open(img_path).convert("RGB").resize((384, 384))
    arr = np.expand_dims(np.array(img, dtype=np.float32), axis=0)
    interp.set_tensor(ind["index"], arr)
    interp.invoke()
    probs = interp.get_tensor(outd["index"])[0]
    top   = np.argsort(probs)[-top_k:][::-1]
    return [(class_map[str(i)], float(probs[i])) for i in top]

results = predict("leaf.jpg")
for cls, conf in results:
    print(f"{cls}: {conf*100:.1f}%")

Option B — Full Keras model

import keras
import numpy as np
import json
from PIL import Image

model     = keras.models.load_model("best_model.keras")
class_map = json.load(open("class_indices.json"))

def predict(img_path: str, top_k: int = 5):
    img  = Image.open(img_path).convert("RGB").resize((384, 384))
    arr  = np.expand_dims(np.array(img, dtype=np.float32), axis=0)
    prob = model.predict(arr, verbose=0)[0]
    top  = np.argsort(prob)[-top_k:][::-1]
    return [(class_map[str(i)], float(prob[i])) for i in top]

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Files

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Limitations

• Trained on controlled laboratory photographs from PlantVillage only
• Generalisation to field photographs with occlusion, variable lighting, or soil contamination is not validated
• Not intended for commercial crop management decisions

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Citation

@misc{kumar2025plantdisease,
  author    = {Animesh Kumar},
  title     = {Plant Disease Detection using EfficientNetV2S},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/animeshakr/plant-disease-efficientnetv2s}
}

References

• Hughes & Salathé (2015). An open access repository of images on plant health. arXiv:1511.08060
• Tan & Le (2021). EfficientNetV2: Smaller Models and Faster Training. ICML 2021
• Gal & Ghahramani (2016). Dropout as a Bayesian Approximation. ICML 2016
• Dietterich (1998). Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation

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Developed by Animesh Kumar