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- Title
Development of a Low-Cost Distributed Computing Pipeline for High-Throughput Cotton Phenotyping.
- Authors
Thesma, Vaishnavi; Rains, Glen C.; Mohammadpour Velni, Javad
- Abstract
In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pis in a primary-replica distributed architecture using the Apache Hadoop ecosystem and a pre-trained Tiny-YOLOv4 model for cotton bloom detection from our past work. We feed cotton image data collected from a research field in Tifton, GA, into our cluster's distributed file system for robust file access and distributed, parallel processing. We then submit job requests to our cluster from our client to process cotton image data in a distributed and parallel fashion, from pre-processing to bloom detection and spatio-temporal map creation. Additionally, we present a comparison of our four-node cluster performance with centralized, one-, two-, and three-node clusters. This work is the first to develop a distributed computing pipeline for high-throughput cotton phenotyping in field-based agriculture.
- Subjects
DEEP learning; DISTRIBUTED computing; RASPBERRY Pi; COTTON; PARALLEL processing; COMPUTER vision
- Publication
Sensors (14248220), 2024, Vol 24, Issue 3, p970
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s24030970