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Garden Guard [ML Model]

Garden Guard [ML Model]

As: ML Engineer

  • Built and trained CNN models in TensorFlow for classifying plant diseases with 94%–98% accuracy.
  • Processed 1,600–3,200 training images and 400–800 validation images per model across 100 epochs.
  • Used Adam optimizer (learning rate: 0.001) for efficient training and optimal performance.
  • Conducted data preprocessing and visualization with NumPy, Matplotlib, and Seaborn.
  • Managed datasets via Google Drive, using Jupyter Notebook and Google Colab for experimentation.
  • Tools: Python, TensorFlow, Keras, NumPy, Matplotlib, Scikit-learn, Google Colab.