supriya-menta/coffee-nutrition

# ☕ Coffee Leaf Nutrition Prediction An advanced AI-powered system designed to identify and diagnose nutrient deficiencies in coffee plants through image analysis of their leaves. This project provides farmers and agricultural experts with instant, actionable insights to ensure optimal crop health. --- ## 📊 System Flowcharts To better understand how the project works, we have broken down the system into three primary workflows. ### 1. High-Level System Architecture This chart shows how the user interacts with the frontend and how the frontend communicates with the AI backend. ```mermaid graph LR User([User]) --> Frontend[Frontend: HTML/CSS/JS] Frontend -- "1. Upload Image (POST)" --> Backend[Backend: Flask API] Backend -- "2. Machine Learning Inference" --> Model[CNN Model: weights.hdf5] Model -- "3. Return Predicted Class + Confidence" --> Backend Backend -- "4. Send JSON Response" --> Frontend Frontend -- "5. Visualize Results & Actions" --> User ``` ### 2. Deep Dive: Prediction Logic Flow This chart explains the exact steps the AI takes to identify whether a leaf is healthy or has a deficiency (N, P, or K). ```mermaid flowchart TD Start([Upload Received]) --> Resizing[Resize Image to 224x224] Resizing --> Preprocessing[Normalize Pixel Values 0-1] Preprocessing --> Inference[Forward Pass through CNN Layers] Inference --> Features[Extract Edges, Textures & Leaf Patterns] Features --> Probabilities[Calculate Probabilities for 4 Classes] Probabilities --> Decision{Highest Probability?} Decision -->|N_Deficiency| N_Res[Nitrogen Deficiency Found] Decision -->|P_Deficiency| P_Res[Phosphorus Deficiency Found] Decision -->|K_Deficiency| K_Res[Potassium Deficiency Found] Decision -->|Healthy| H_Res[Healthy Leaf Detected] N_Res & P_Res & K_Res & H_Res --> Output[Return Result to User] ``` ### 3. Application Navigation Flow This chart displays the different routes available in the web application and what users can do in each section. ```mermaid graph TD Root[/] --> H …