Agrox AI 🌾

Problem Statement

Farmers face losses due to unpredictable weather, pests, and crop diseases. Manual monitoring is inefficient, time-consuming, and often reactive.

Goal

Agrox AI aims to:

• Detect crop diseases from leaf images using CNN models.
• Predict crop yield based on soil, weather, and historical data.
• Provide recommendations for fertilizers, pesticides, and irrigation using ML.

Project Phases

1. Phase 1: Leaf image classification for disease detection.
2. Phase 2: ML model for yield prediction using weather and soil data.
3. Phase 3: Recommendation engine for fertilizers and pesticides.
4. Phase 4: Farmer-friendly web/mobile app with regional language support.

Tech Stack

• Frontend: HTML, CSS, JavaScript
• Backend: Python (Flask)
• AI/ML: TensorFlow/Keras

Outcome

• Early detection of crop diseases
• Accurate yield prediction
• Personalized farming recommendations
• Improved efficiency and reduced losses for farmers