🌱 Plant Health & Identification API

An intelligent backend service built for our hackathon project. This API uses a dual-model AI system to analyze an image of a plant, first identifying its species and then assessing its health.

🚀 The Problem

Have you ever wondered what kind of plant you have or why its leaves are suddenly turning yellow? Our project aims to provide a simple, accessible answer. This repository contains the backend API that powers our application, capable of providing a comprehensive plant analysis from a single image.

🧠 The AI Pipeline

This API uses a two-stage process to analyze an image:

Species Identification: We use a custom-trained TensorFlow/Keras model, built on a MobileNetV2 architecture. This model was fine-tuned on a dataset of six specific plant types to achieve high accuracy in identifying which plant is in the image.

Health Assessment: Once the plant's species is known (e.g., "tomato"), we use OpenAI's powerful, open-source CLIP model. We dynamically generate text prompts like "a photo of a healthy tomato plant" or "a photo of a sick tomato plant with yellow spots" and ask CLIP which description best matches the image. This zero-shot approach allows for a flexible and nuanced understanding of the plant's health.

✨ Features

Identify 6 Plant Species: Accurately distinguishes between tomato, basil, mint, lettuce, rosemary, and strawberry.

Assess 4 Health States: Classifies plants as Healthy, Diseased, Dehydrated, or Dead.

Confidence Scores: Provides confidence levels for both the species identification and the health assessment.

Simple JSON API: Easy to integrate with any frontend or mobile application.

🛠️ Tech Stack

Backend Framework: Flask

AI / Machine Learning: TensorFlow (Keras), PyTorch, OpenAI CLIP

Image Processing: Pillow, OpenCV

Core Language: Python 3.10

🔌 API Documentation

This is the documentation for the main analysis endpoint.

Analyze a Plant Image

Endpoint: /analyze

Method: POST

Body: multipart/form-data

The request must contain a file field named image.

Successful Response (Status 200 OK)

The API will return a JSON object with the analysis.

Example Response:

Generated json
{
  "plant_species": "tomato",
  "identification_confidence": "97.45%",
  "health_status": "Healthy",
  "health_confidence": "89.12%",
  "health_breakdown": {
    "Healthy": 0.8912,
    "Diseased": 0.0562,
    "Dehydrated": 0.0421,
    "Dead": 0.0105
  }
}


Field Descriptions:

Key	Type	Description
plant_species	String	The identified species of the plant.
identification_confidence	String	The model's confidence in the species identification.
health_status	String	The most likely health status of the plant.
health_confidence	String	The model's confidence in the health assessment.
health_breakdown	Object	A dictionary of raw probability scores for each health state.
Error Responses

If the request is missing an image, the API will return:

Status: 400 Bad Request

Body: {"error": "No image file provided"}

For any other server-side issues, the API will return:

Status: 500 Internal Server Error

Body: {"error": "An internal server error occurred."}

🖥️ How to Run Locally

To run this backend server on your own machine, follow these steps.

Clone the Repository:

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git clone <repository-url>
cd <repository-name>/backend
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Create a Virtual Environment:

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python3 -m venv venv
source venv/bin/activate
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Install Dependencies:
This can take a while as it will download TensorFlow and PyTorch.

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pip install -r requirements.txt
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Place the Model:
Make sure you have the trained Keras model (BestModel.keras) inside the models/ directory.

Run the Server:

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python app.py
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The API will now be running on your local machine at http://127.0.0.1:5000.

📁 Project Structure
Generated code
/backend
|-- app.py             # The main Flask server and API logic.
|-- models/            # Folder for the trained Keras model.
|   |-- BestModel.keras
|-- requirements.txt   # Python dependencies.
|-- .gitignore         # Files to be ignored by Git (like the venv).
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👥 Authors

[Your Name]

[Teammate's Name]

[Teammate's Name]