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Real-time high-throughput cotton phenotyping using distributed computing and deep learning
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DOI：10.1016/j.atech.2025.101383.png)
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Abstract
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• Development of an open-source distributed computing architecture for high-throughput cotton plant phenotyping. • Distributed cluster uses a cluster of Raspberry Pi 4's, Apache Hadoop, Apache Spark, OpenCV, and tiny-YOLO. • Apache Spark provides near real-time processing of cotton image data on a cluster of Raspberry Pi 4's. • Cotton image data from field environment are processed using OpenCV and a pre-trained tiny-YOLO model deployed onto cluster. • Developed cluster is capable of processing cotton image data in parallel.
Keywords:
Big data
Distributed computing
Cotton phenotyping
Computer vision
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