API Reference

The Plant Disease Predictor API exposes a single prediction endpoint that accepts a leaf image file and returns the identified disease class name. Use this endpoint whenever you need to programmatically submit plant leaf images for analysis and retrieve structured disease predictions from the underlying CNN model.

On a successful prediction, the API returns the predicted disease class name as a plain string. Internally, the model produces a probability distribution over all known disease classes (loaded from class_indices.json), and the class with the highest probability is selected via argmax.

Successful response fields:

The prediction is only returned after the user (or calling client) triggers classification. If no image has been uploaded, the endpoint returns no prediction output.

Example 1 — Submit a leaf image using curl

This example uploads a JPEG leaf image and retrieves the disease prediction.

curl -X POST "http://localhost:8501/predict" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@tomato_leaf.jpg"

Expected output:

{
  "prediction": "Tomato___Early_blight"
}

Example 2 — Submit a leaf image using Python requests

This example demonstrates how to call the endpoint from a Python script, which is useful when integrating the predictor into a larger agricultural monitoring pipeline.

import requests

url = "http://localhost:8501/predict"

with open("tomato_leaf.jpg", "rb") as image_file:
    response = requests.post(
        url,
        files={"file": ("tomato_leaf.jpg", image_file, "image/jpeg")}
    )

if response.status_code == 200:
    result = response.json()
    print(f"Predicted disease: {result['prediction']}")
else:
    print(f"Error: {response.status_code} — {response.text}")

Expected output:

Predicted disease: Tomato___Early_blight

Example 3 — Uploading a PNG image

PNG files are accepted alongside JPEG. The preprocessing pipeline handles both formats identically.

curl -X POST "http://localhost:8501/predict" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@pepper_leaf.png"

Expected output:

{
  "prediction": "Pepper,_bell___Bacterial_spot"
}

• Image preprocessing is automatic. Every image you upload is resized to 224 × 224 pixels and normalized to the [0, 1] float range before it reaches the CNN model. You do not need to preprocess images on the client side, but submitting very low-resolution or heavily cropped images may reduce prediction accuracy.
• Accepted formats are strictly jpg, jpeg, and png. Other formats such as webp, bmp, or tiff are not accepted by the file uploader, even if Pillow could theoretically decode them.
• The model file must be present at startup. The CNN model (plant_disease_prediction_model.h5) and the class index mapping (class_indices.json) are loaded once when the application initializes. Changes to these files require a server restart to take effect.
• Only one prediction is returned per request. The model selects the single highest-confidence class using argmax. Raw probability scores or ranked alternatives are not exposed through the current API surface.
• No authentication is implemented in the source. The application as written does not enforce API keys, OAuth tokens, or any other authentication mechanism. If you are deploying this in a production or multi-tenant environment, add an authentication layer at the infrastructure level (e.g., an API gateway or reverse proxy) before exposing the endpoint publicly.
• Batch inference is not supported. Each request must contain a single image file. To classify multiple images, issue one request per image.
• Class names reflect the training dataset labels. The disease names returned (e.g., "Tomato___Early_blight") use the naming convention from the dataset used to train the model. Consult your class_indices.json file for the full list of supported disease classes.