2025-07-16 22:49:20 +01:00
2025-07-03 15:27:59 +01:00
2025-07-03 15:27:59 +01:00

Smart Farm Photo Keyword Tagging AI

Project Overview

This project aims to automate the generation of high-quality, agriculture-relevant keyword tags for agricultural stock photos using AI. The system will replace the current manual keyword tagging process, saving significant time and improving consistency.

What is Expected

  • AI Model: A model trained to generate 510 relevant, high-quality keywords per image, with a focus on agricultural context and subtle distinctions (e.g., farmer vs. rancher, male vs. female farmer).
  • Title Generation: Optionally generate a descriptive product title for each photo (e.g., "Farmer and son walking in cornfield").
  • Location Extraction: If location metadata is present in the image, extract and use it as a keyword (e.g., "Iowa").
  • CSV Output: For each photo, output a CSV row with:
    • Photo file name
    • Human-entered keywords (for comparison)
    • AI-generated keywords
    • AI-generated title (if available)
    • Location (if available)
  • Training: The system should be trainable on a dataset of ~30,000 currently keyword-tagged photos.
  • Scalability: Should handle at least 1,000 photos/month (in batches of 500), with potential to double in 3 years.
  • Quality: Keywords and titles must be accurate, relevant, and reflect subtle ag-specific concepts.

🚀 Quick Start

Option 1: Professional Web Interface (Recommended)

# Start the web interface
python3 web_interface.py

# Open browser to http://localhost:8000
# - Drag and drop agricultural photos
# - See real-time AI processing with image previews
# - View quality scores and keywords

Option 2: Command Line

# 1. Install dependencies
python3 -m pip install -r requirements.txt

# 2. Run the system
python3 src/main.py

# 3. Check results
cat outputs/agricultural_keywords_*.csv

Option 3: Team Demonstration

# Run comprehensive team demo
python3 team_demonstration.py

🌐 Web Interface Features

  • Professional UI: Clean, responsive design with agricultural theme
  • Image Preview: See actual photos being processed with results
  • Real-time Processing: Watch AI generate keywords in real-time
  • Quality Scores: Visual quality indicators for generated content
  • API Documentation: Interactive Swagger/OpenAPI docs
  • Demo Mode: Test with sample agricultural images

Folder Structure

.
├── data/         # Datasets: training, validation, test images, and CSVs
│   ├── raw/      # Raw, unprocessed images and metadata
│   ├── processed/# Preprocessed data ready for modeling
│   └── ...
├── notebooks/    # Jupyter notebooks for EDA, prototyping, and experiments
├── src/          # Source code
│   ├── data/     # Data loading, preprocessing scripts
│   ├── model/    # Model architecture, training, inference code
│   ├── utils/    # Utility functions
│   └── main.py   # Main entry point for training/inference
├── outputs/      # Generated outputs (CSVs, predictions, logs)
├── docs.txt      # Project requirements and notes
├── README.md     # Project overview and instructions
└── .gitignore    # Files and folders to ignore in git

Directory Details

  • data/: All datasets. Use raw/ for original files, processed/ for cleaned/ready-to-use data.
  • notebooks/: Jupyter notebooks for data exploration, prototyping, and model development.
  • src/: All source code, organized by function (data, model, utils). main.py is the main script.
  • outputs/: All generated outputs, including CSVs with AI-generated tags/titles, logs, and model predictions.
  • docs.txt: The original requirements and project notes.
  • README.md: This file.
  • .gitignore: Keeps unnecessary files out of version control.

Deliverables - ALL COMPLETED

  • Well-documented code in src/ - Complete modular architecture
  • Professional web interface - Full UI with image display and real-time processing
  • Complete REST API - Comprehensive API with interactive documentation
  • Jupyter notebook - EDA and model prototyping completed
  • Example CSV output - Multiple working examples with quality validation
  • Instructions for running - Multiple usage options documented
  • Complete training pipeline - Ready for 30,000 photo dataset
  • Team demonstration script - Professional presentation tool

🎯 System Status: PRODUCTION READY

The Smart Farm Photo Keyword Tagging AI system is 100% complete and ready for immediate use!

S
Description
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Readme 6.2 MiB
Languages
Python 91%
Jupyter Notebook 9%