4.6 KiB
🌱 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:
Generated bash git clone cd /backend IGNORE_WHEN_COPYING_START content_copy download Use code with caution. Bash IGNORE_WHEN_COPYING_END
Create a Virtual Environment:
Generated bash python3 -m venv venv source venv/bin/activate IGNORE_WHEN_COPYING_START content_copy download Use code with caution. Bash IGNORE_WHEN_COPYING_END
Install Dependencies: This can take a while as it will download TensorFlow and PyTorch.
Generated bash pip install -r requirements.txt IGNORE_WHEN_COPYING_START content_copy download Use code with caution. Bash IGNORE_WHEN_COPYING_END
Place the Model: Make sure you have the trained Keras model (BestModel.keras) inside the models/ directory.
Run the Server:
Generated bash python app.py IGNORE_WHEN_COPYING_START content_copy download Use code with caution. Bash IGNORE_WHEN_COPYING_END
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). IGNORE_WHEN_COPYING_START content_copy download Use code with caution. IGNORE_WHEN_COPYING_END 👥 Authors
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