How to Use the Crop Disease Identification Detection API

Use this pre-trained Crop Disease Identification computer vision model to retrieve predictions with our hosted API or deploy to the edge. Learn More About Roboflow Inference

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v4
crop-disease-identification/4
v9
crop-disease-identification/9
Trained On: 2024-04-17-11-42pm
v8
crop-disease-identification/8
Trained On: 2024-04-12-11-49pm
v6
crop-disease-identification/6
Trained On: 2024-04-10-6-27pm
v5
crop-disease-identification/5
Trained On: 2024-04-10-1-50pm
v4
crop-disease-identification/4
Trained On: 2024-04-10-12-47am
v2
crop-disease-identification/2
Trained On: 2024-04-09-9-04am
v1
crop-disease-identification/1
Trained On: 2024-04-09-8-11am
Trained On: crop-disease-identification 5315 Images
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Model Type: Roboflow 3.0 Instance Segmentation (Fast)
mAP
37.4%
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Roboflow Inference

Inference is Roboflow's open source deployment package for developer-friendly vision inference.

How to Deploy the Crop Disease Identification Detection API

Using Roboflow, you can deploy your object detection model to a range of environments, including:

  • Raspberry Pi
  • NVIDIA Jetson
  • A Docker container
  • A web page
  • A Python script using the Roboflow SDK.

Below, we have instructions on how to use our deployment options.

Code Snippets

Hosted API
Utilities
Python
JavaScript
## Infer on Local and Hosted Images To install dependencies, `pip install inference-sdk`. Then, add the following code snippet to a Python script: ```python from inference_sdk import InferenceHTTPClient CLIENT = InferenceHTTPClient( api_url="https://outline.roboflow.com", api_key="API_KEY" ) result = CLIENT.infer(your_image.jpg, model_id="MODEL_ENDPOINT/VERSION") ``` [See the inference-sdk docs](https://inference.roboflow.com/inference_helpers/inference_sdk/)
## Node.js We're using [axios](https://github.com/axios/axios) to perform the POST request in this example so first run npm install axios to install the dependency. ### Inferring on a Local Image ```javascript const axios = require("axios"); const fs = require("fs"); const image = fs.readFileSync("YOUR_IMAGE.jpg", { encoding: "base64" }); axios({ method: "POST", url: "https://outline.roboflow.com/MODEL_ENDPOINT/VERSION", params: { api_key: "API_KEY" }, data: image, headers: { "Content-Type": "application/x-www-form-urlencoded" } }) .then(function (response) { console.log(response.data); }) .catch(function (error) { console.log(error.message); }); ``` ### Inferring on an Image Hosted Elsewhere via URL ```javascript const axios = require("axios"); axios({ method: "POST", url: "https://outline.roboflow.com/MODEL_ENDPOINT/VERSION", params: { api_key: "API_KEY", image: "IMAGE_URL" } }) .then(function (response) { console.log(response.data); }) .catch(function (error) { console.log(error.message); }); ```
More Deployment Resources
Roboflow Inference Documentation

Look through our Inference documentation for more information and resources on how to utilize this model.
Example Web App

Use this model with a full fledged web application that has all sample code included.
Deploy to NVIDIA Jetson

Perform inference at the edge with a Jetson via our Docker container.
Deploy Mobile iOS

Utilize your model on your mobile device.

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