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Roboflow supports deployment in the following environments: Raspberry Pi, NVIDIA Jetson, a Docker container, a web page, iOS, and a Python script using the Roboflow SDK.

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FRUIT DETECTION Computer Vision Model

byfruit detectionTask:

Object Detection

License:CC BY 4.02.2k views82 downloads

How to use the FRUIT DETECTION Detection API

Try This Model

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Code Snippets

from inference_sdk import InferenceHTTPClient

CLIENT = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key="API_KEY"
)

result = CLIENT.infer("YOUR_IMAGE.jpg", model_id="fruit-detection-deqvb/1")

Give your agent everything it needs

Or, Use Free APPLE, TOMATO and WATERMELON Detection API

Code

pip install inference-sdk

# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workspace
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="<YOUR_WORKSPACE>",
  workflow_id="<YOUR_WORKFLOW_ID>",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": "APPLE, TOMATO, WATERMELON, ONIONS, PINEAPLE"
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Run on custom image

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Detecting classes:

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See all 414 images

FRUIT DETECTION Fruits sample showing PINEAPLE

FRUIT DETECTION Fruits sample showing APPLE

FRUIT DETECTION Fruits sample showing TOMATO

FRUIT DETECTION Fruits sample showing WATERMELON

About FRUIT DETECTION Model

Here are a few use cases for this project:

Farm Automation: The model could be used in smart farming applications to identify and categorize fruits during harvesting. This could help automate the harvesting process and increase the overall productivity of the farm.
Grocery Store Management: The model could be integrated into inventory management systems at grocery stores to automatically identify and count various types of fruits. This would simplify stock tracking, aid in automatic pricing, and reduce manual labor costs.
Health and Nutrition Apps: The model could be incorporated into health and nutrition apps aimed at helping users track their food intake. Users could take pictures of their meals, and the app could identify any fruit present, aiding in the calculation of nutritional intake.
Food Safety and Quality Inspection: The model could be used in food processing industries to identify and remove non-fruit objects or unfit fruits from the production line, thus ensuring the quality and safety of the products.
Educational Tools: The model could be included in interactive educational software or mobile apps aimed at teaching children about different types of fruits and their nutritional benefits.

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Cite This Project

LicenseCC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ fruit-detection-deqvb_dataset,
  title = { FRUIT DETECTION Dataset },
  type = { Open Source Dataset },
  author = { fruit detection },
  howpublished = { \url{ https://universe.roboflow.com/fruit-detection-w707e/fruit-detection-deqvb } },
  url = { https://universe.roboflow.com/fruit-detection-w707e/fruit-detection-deqvb },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2023 },
  month = { mar },
  note = { visited on 2026-07-29 },
}

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