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References: Data Visualization and Process Control

  1. Plotly - Wikipedia - Overview of the Plotly open-source visualization library, covering interactive charts, Plotly Express high-level API, Plotly Dash web framework, and real-time data streaming capabilities used for hydroponic dashboards.

  2. Control chart - Wikipedia - Explains Shewhart X-bar and R control charts, control limit calculation (±3σ), Western Electric rules for detecting non-random patterns, and process capability analysis applicable to monitoring pH and EC stability.

  3. Matplotlib - Wikipedia - Overview of the Matplotlib Python visualization library including its architecture, pyplot interface, object-oriented API, and use cases for static time-series plots and export-quality figures for reports.

  4. Python for Data Analysis (3rd ed.) - Wes McKinney - O'Reilly Media - Visualization chapters cover Matplotlib integration with pandas, time-series plot formatting, subplots, and annotation techniques for creating publication-quality hydroponic data figures.

  5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - Aurélien Géron - O'Reilly Media - Covers anomaly detection algorithms, Z-score flagging, moving average deviation, and dashboard design patterns applicable to building automated alert systems for hydroponic process control.

  6. Plotly Python Documentation - Plotly.com - Official Plotly Python library documentation covering line charts, scatter plots, heatmaps, subplots, and real-time streaming — directly applicable to building interactive hydroponic monitoring dashboards.

  7. Plotly Dash Documentation - Plotly.com - Complete reference for the Plotly Dash web application framework, including Interval components for real-time updates, callback functions, and layout design for live hydroponic sensor dashboards.

  8. Real Python: Matplotlib Guide - Real Python - Comprehensive tutorial on Matplotlib covering figure/axes architecture, plot types, styling, and annotation; applicable to creating time-series analysis plots for hydroponic pH, EC, and temperature data.

  9. pandas: Visualization - pandas.pydata.org - Official pandas visualization documentation covering DataFrame.plot() shortcuts, rolling mean overlays, and correlation heatmaps that simplify exploratory analysis of hydroponic sensor datasets.

  10. Khan Academy: Statistics and Probability - Khan Academy - Covers standard deviation, normal distributions, outlier detection, and data visualization principles foundational for interpreting control charts and anomaly flags in hydroponic monitoring systems.