danarfthr/HealthyHen

🐔 EfficientNetV2S Poultry Feces Classifier

A convolutional neural network model based on EfficientNetV2S for classifying chicken fecal images into 4 common conditions:

• Coccidiosis
• Healthy
• Newcastle Disease
• Salmonella

This model is designed to support smart poultry farming by enabling early detection of diseases through image-based feces analysis.

🧬 Model Architecture

• Base: EfficientNetV2S (pretrained on ImageNet, frozen then fine-tuned)

• Head:

  • GlobalAveragePooling2D
  • Dense(128) + BatchNorm + ReLU + Dropout(0.3)
  • Dense(4, activation='softmax')

🧪 Training & Evaluation

• Optimizer: Adam

• Loss: Categorical Crossentropy

• Metric: Accuracy

• Dataset:

  • Source: Jayavrinda et al., 2023
  • 4 classes, resized to 224x224 pixels
  • Train/Val/Test sampling (3k/400/400 per class)

• EarlyStopping was used to monitor validation accuracy

• Accuracy on validation set: ~90%+ (see notebook for full results)

🗄️ Example Usage

from tensorflow.keras.models import load_model
import tensorflow as tf
from PIL import Image
import numpy as np

model = load_model("path/to/your_model.h5")

def preprocess(image_path):
    img = Image.open(image_path).resize((224, 224))
    img_array = np.array(img) / 255.0
    return np.expand_dims(img_array, axis=0)

pred = model.predict(preprocess("feces.jpg"))
class_names = ["Coccidiosis", "Healthy", "Newcastle", "Salmonella"]
print("Prediction:", class_names[np.argmax(pred)])

📜 Citation

If you use this model or dataset, please cite:

Jayavrinda Vrindavanam, Pradeep Kumar, Gaurav Kamath, Chandrashekar N, and Govind Patil. (2023). Poultry Pathology Visual Dataset [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DS/3951043

---

Beyond the Outliers

Datathon 2025