High-throughput Cotton Phenotyping Big Data Pipeline Lambda Architecture
  Computer Vision Deep Neural Networks
Amanda Issac Alireza Ebrahimi Javad Mohammadpour Velni Glen Rains
In this study, we propose a big data pipeline for cotton bloom detection
using a Lambda architecture, which enables real-time and batch processing of
data. Our proposed approach leverages Azure resources such as Data Factory,
Event Grids, Rest APIs, and Databricks. This work is the first to develop and
demonstrate the implementation of such a pipeline for plant phenotyping through
Azure's cloud computing service. The proposed pipeline consists of data
preprocessing, object detection using a YOLOv5 neural network model trained
through Azure AutoML, and visualization of object detection bounding boxes on
output images. The trained model achieves a mean Average Precision (mAP) score
of 0.96, demonstrating its high performance for cotton bloom classification. We
evaluate our Lambda architecture pipeline using 9000 images yielding an
optimized runtime of 34 minutes. The results illustrate the scalability of the
proposed pipeline as a solution for deep learning object detection, with the
potential for further expansion through additional Azure processing cores. This
work advances the scientific research field by providing a new method for
cotton bloom detection on a large dataset and demonstrates the potential of
utilizing cloud computing resources, specifically Azure, for efficient and
accurate big data processing in precision agriculture.