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HealthyHen

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🐔 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

python

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

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danarfthr/HealthyHen