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Development of a Low-Cost Distributed Computing Pipeline for High-Throughput Cotton Phenotyping.

Vaishnavi Thesma1, Glen C Rains2, Javad Mohammadpour Velni3

  • 1School of Electrical and Computer Engineering, University of Georgia, Athens, GA 30602, USA.

Sensors (Basel, Switzerland)
|February 10, 2024
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Summary
This summary is machine-generated.

Researchers developed a low-cost distributed computing pipeline for cotton phenotyping using Raspberry Pi and Hadoop. This system enables efficient, high-throughput analysis of cotton images for improved agricultural research.

Keywords:
big datacomputer visioncotton phenotypingdistributed computing

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Data Science

Background:

  • High-throughput phenotyping is crucial for crop improvement.
  • Existing methods can be costly and computationally intensive.
  • Distributed computing offers a scalable solution for agricultural data analysis.

Purpose of the Study:

  • To develop a low-cost, distributed computing pipeline for cotton plant phenotyping.
  • To leverage Raspberry Pi, Hadoop, and deep learning for efficient image analysis.
  • To enable high-throughput phenotyping in field-based agriculture.

Main Methods:

  • Utilized a primary-replica distributed architecture with Raspberry Pis and Apache Hadoop.
  • Employed a pre-trained Tiny-YOLOv4 model for cotton bloom detection.
  • Implemented distributed file system for robust data access and parallel processing.
  • Evaluated cluster performance with varying node configurations.

Main Results:

  • Successfully developed and implemented a functional distributed computing pipeline.
  • Demonstrated parallel processing of cotton image data from pre-processing to bloom detection.
  • Provided performance comparisons of four-node vs. centralized and smaller clusters.
  • Achieved efficient spatio-temporal map creation for cotton phenotyping.

Conclusions:

  • The developed pipeline offers a cost-effective solution for high-throughput cotton phenotyping.
  • This work pioneers distributed computing applications in field-based agricultural research.
  • The system facilitates robust and scalable analysis of agricultural imagery.