Plant Disease Detection Model

I am seeking an experienced deep learning specialist to build a robust image classification model for detecting plant diseases from leaf images. I already have a clean, ready-to-use dataset of plant leaf images in JPEG format, categorized by disease types.

The goal is to train a high-accuracy deep learning model that can classify plant diseases across multiple species (e.g., tomato, potato, apple, grape, corn) using Convolutional Neural Networks (CNN).

Key Deliverables:

Build and train a CNN-based image classification model

Use the provided labeled JPEG dataset

Optimize for accuracy, speed, and generalization

Evaluate and test model performance (confusion matrix, accuracy, etc.)

Provide well-documented code and trained model files

(Optional) Basic integration with a simple UI using Flask or Streamlit

Ideal Skills & Experience:

Strong background in Deep Learning and Computer Vision

Proven experience with image classification tasks

Proficiency in Python, TensorFlow, or PyTorch

Knowledge of data augmentation, transfer learning, and model evaluation

Experience with plant/crop datasets is a plus