Counting Apples and Oranges using Raspberry pi and OpenCV

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Description

Counting Apples and Oranges

Counting Apples and Oranges using Raspberry pi and OpenCV – Fruit counting is an important task for growers to estimate yield and manage orchards. An accurate automated fruit detection and counting algorithm give agricultural enterprises the ability to optimize and streamline their harvest process. Through a better understanding of the variability of yield across their farmlands, growers can make more informed and cost-effective decisions for labor allotment, storage, packaging, and transportation. Estimation of fruit count from images is a challenging task for a number of reasons including appearance variability due to illumination, and occlusion due to surrounding foliage and fruits. Fruit counting algorithms relied on Open computer vision methods involving hand-crafted features that exploited the shape, color, texture, or spatial orientation of various fruit. A counting algorithm based on a second convolution network then estimates the number of fruit in each region. Finally, color maps that fruit count estimate to a final fruit count. This method generalizes


INTRODUCTION

Counting Apples and Oranges

Fruit counting is an important task for growers to estimate yield and manage orchards. An accurate automated fruit detection and counting algorithm give agricultural enterprises the ability to optimize and streamline their harvest process. Through a better understanding of the variability of yield across their farmlands, growers can make more informed and cost-effective decisions for labor allotment, storage, packaging, and transportation. Smart sensor suites, as well as autonomous robots such as unmanned aerial vehicles (UAVs), will benefit from data-driven fruit counting algorithms that enable growers to estimate yield at scale across both data sets and is able to perform well even on highly occluded fruits that are challenging for human labelers to annotate.

EXISTING SYSTEM

  • Fruit counting algorithms relied on traditional MATLAB based process
  • Manually Counting
  • Image segmentation Can be done

DISADVANTAGES

  • It will take high processing Time
  • Recognize only Fixed shape, color, texture

PROPOSED SYSTEM

  • Open CV based Object Recognition and Counting
  • Deep learning-based Approach

BLOCK DIAGRAM

Counting Apples and Oranges using Raspberry pi and OpenCV

CIRCUIT DIAGRAM

Counting Apples and Oranges using Raspberry pi

BLOCK DIAGRAM EXPLANATION

The system consists of a Cortex A-53 Raspberry Pi device and camera. Here camera is used to capture the image frames; then, it will compare to the related fruits. If it’s recognized means count is increased one. Image reorganized process based on Open computer vision Algorithms.

Counting Apples and Oranges


HARDWARE REQUIREMENTS

  • Raspberry pi
  • SD card
  • Camera

SOFTWARE REQUIREMENTS

  • Raspbian Jessie OS
  • OpenCV
  • Language: Python

CONCLUSION

We have presented a novel data-driven end-to-end fruit counting pipeline based on deep learning that generalizes across various unstructured environments. In order to demonstrate this generalization, we chose data sets that are challenging in unique ways: the orange data set features a high level of occlusions, depth variation, and uncontrolled illumination, and the apple data set features high color similarity between fruit and foliage


 

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