Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Real-World Object Detection: Waste Sorting & Tomato Ripeness
Rating: 4.6 out of 5(9 ratings)
862 students
Created bySuman Kunwar
Last updated 4/2026
English

What you'll learn

  • Build real-world object detection models using YOLOv10 for waste sorting and tomato ripeness detection.
  • Annotate custom image datasets using Annotate-Lab in YOLO format for object detection tasks.
  • Apply data augmentation techniques to improve model accuracy and robustness.
  • Deploy trained models to the web and mobile using Gradio, Hugging Face and Flutter for real-time AI applications.

Explore related topics

Course content

2 sections • 22 lectures • 56m total length

  • Introduction to Tomato Ripeness Detection1:50
  • Building a Tomato Dataset: Ripe vs. Unripe Images1:18
  • Annotating Tomato Images with Annotate-Lab2:15
  • COCO Annotations1:22
  • EDA for Tomato Ripeness Detection4:06
  • Model Evaluation and Metrics3:05
  • YOLOv10 Architecture Explained3:13
  • Training YOLOv10 for Tomato Detection2:34
  • Early Stopping to Capture the Best Value3:03
  • Exporting the Model for Mobile Deployment1:44
  • Quantization for Faster Inference1:11
  • Deploying Tomato Detection to the Web (Hugging Face)1:07
  • Counting Ripe & Unripe Tomatoes1:23
  • Measuring Operational Carbon Footprints of the AI Model1:53
  • Post-Optimization Techniques2:57

Requirements

  • Basic Python knowledge is helpful, but the course is beginner-friendly and guides you step-by-step.
  • A computer with internet access and basic familiarity with running Python scripts.
  • Interest in AI, computer vision, or sustainability-related projects.

Description

Are you ready to apply computer vision to real-world problems?
In this hands-on course, you’ll build two complete object detection projects: one for identifying household waste items (like plastic, glass, and paper), and another for detecting ripe and unripe tomatoes using the latest YOLOv10 model.

We’ll walk you through each step of the pipeline from dataset preparation and annotation to training and deploying your own AI models. You'll gain practical experience with tools like Annotate Lab, Gradio, and Ultralytics YOLO, while also learning how data augmentation and evaluation metrics can improve model performance.

Whether you're interested in sustainability, agriculture, or real-time AI applications, this course provides both the theory and implementation you need to bring AI to life.

By the end of this course, you will:

  • Train a YOLOv10 model to detect ripe vs. unripe tomatoes

  • Build an object detector for sorting waste categories

  • Annotate images using Annotate-Lab with YOLO format

  • Apply data augmentation to boost performance

  • Deploy your model using Gradio on Hugging Face Spaces

  • Export and run your model on mobile devices (optional module)

This course is ideal for:

  • Developers and data scientists curious about object detection

  • Environmental and agri-tech enthusiasts

  • Anyone looking to learn YOLOv10 with practical projects

Enroll today and build AI tools that make an impact from waste bins to tomato fields.

Who this course is for:

  • Beginners and intermediate learners interested in applying computer vision to real-world problems.
  • Developers, students, and enthusiasts looking to build practical AI projects in sustainability and agriculture.
  • Educators and researchers interested in hands-on projects for waste detection and smart farming.
  • Anyone curious about YOLO, object detection, or using AI for environmental impact.

Report abuse