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Apple Quality Detection with YOLO

Project Overview

The Apple Quality Detection with YOLO project involved developing an Android app for real-time apple classification using the YOLO Object Detection model. The app classifies apples into three categories: fallen apples, damaged apples, and good apples, to aid farmers in assessing apple production and quality.

Objective

The goal of this project was to assist farmers in tracking apple quality and improve agricultural efficiency through the real-time classification of apples. Objectives included:
  • Automated Apple Quality Assessment: To develop an app that can automatically classify apples based on quality, providing insights for farmers.
  • Real-Time Object Detection: To use YOLO Object Detection to classify apples as good, damaged, or fallen, with minimal delay.
  • Mobile Accessibility: To ensure the solution was accessible on mobile devices, making it easy for farmers to use the app in the field.

Approach

To meet these objectives, Annotationworkforce followed these key strategies:

1. YOLO Model Integration

  • Integrated YOLO (You Only Look Once), an efficient object detection model, into the app for real-time apple classification, ensuring it could distinguish between different apple categories.

2. Model Optimization for Mobile

  • Converted the YOLO model to TensorFlow Lite format for mobile deployment, ensuring fast processing without draining device resources.

3. Seamless Android Integration

  • Worked closely with app developers to integrate the YOLO model into the Android app, ensuring smooth functionality and real-time detection.

Problems & Solutions

Problem 1: Real-Time Classification on Mobile Devices

  • Challenge: Real-time object detection on mobile devices can be computationally intensive, especially with large models like YOLO.
  • Solution: We converted the YOLO model to TensorFlow Lite, optimizing the model for real-time performance on mobile devices, ensuring that apple classification occurred without delays.

Problem 2: Ensuring Accurate Apple Classification

  • Challenge: Apples can vary in size, color, and shape, which could make accurate classification challenging.
  • Solution: We fine-tuned the YOLO model using a large dataset of apple images, ensuring high classification accuracy for different apple types and conditions.

Results

1. Increased Agricultural Efficiency: Farmers were able to track and predict apple production more effectively, leading to improved crop management.
2. Quality Control: The app accurately identified good and damaged apples, assisting in quality control and reducing waste.
3. Data-Driven Insights: The app provided farmers with actionable insights into apple quality, enhancing their decision-making and operations.