The Insect Classification System is a deep learning-based application that identifies and classifies insects (pests) from images. This project leverages Convolutional Neural Networks (CNNs) to automatically detect and categorize insects, which can be useful in agriculture for pest control and crop protection.
This project demonstrates the practical application of deep learning in agriculture for real-world pest detection.
- 🧠 Image classification using CNN
- 📷 Upload insect images for prediction
- ⚡ Fast and accurate predictions
- 🌱 Useful for agricultural pest detection
- 🔍 Supports multiple insect classes
- Programming Language: Python
- Deep Learning Framework: TensorFlow / Keras (update if needed)
- Libraries: NumPy, OpenCV, Matplotlib
- Model Type: Convolutional Neural Network (CNN)
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Architecture: Convolutional Neural Network (CNN)
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Layers:
- Convolution + ReLU
- MaxPooling
- Fully Connected Layers
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Loss Function: Categorical Crossentropy
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Optimizer: Adam
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Evaluation Metric: Accuracy
git clone https://github.com/your-username/insect-classification.git
cd insect-classification
pip install -r requirements.txtpython train.pypython predict.py --image path_to_image- Achieved high accuracy on test dataset
- Successfully classifies multiple insect species
(Add your actual accuracy here — important for interviews)
- Input: Image of insect
- Output: Predicted class (e.g., Aphid, Beetle, Caterpillar)
- Deploy as a web application (FastAPI / Flask)
- Add real-time detection using camera
- Improve dataset for better accuracy
- Integrate with mobile app for farmers
Contributions are welcome! Feel free to fork the repo and submit a pull request.
Vivek Kumar
- Open-source datasets
- TensorFlow & Keras community