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

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.

Plant phenotyping relevance

綿花の花の検出を対象とする高スループット表現型解析用の分散計算パイプラインを開発し、異なるクラスタ構成の性能比較も行っており、表現型取得・処理手法が中心である。

Code and data availability

The paper describes cotton image data, a Tiny-YOLOv4 model, and MapReduce pipeline code, but no public dataset or model deposit is provided. The authors state the experiment code 'will be made available on our lab website' with no URL, so code access requires contacting the authors. The only GitHub link (sozykin/mipr)是

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