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References: Data Collection and Analysis

  1. Pandas (software) - Wikipedia - Overview of the pandas Python library for data manipulation, covering DataFrame structures, time-series indexing, groupby operations, and rolling statistics used to analyze hydroponic sensor logs.

  2. Time series - Wikipedia - Mathematical treatment of time-indexed data including stationarity, autocorrelation, trend decomposition, and resampling — foundational concepts for analyzing pH, EC, and temperature logs from hydroponic systems.

  3. Statistical process control - Wikipedia - Covers Shewhart control charts, control limits, Western Electric rules, and process capability indices used to detect drift and anomalies in continuous hydroponic monitoring data.

  4. Python for Data Analysis (3rd ed.) - Wes McKinney - O'Reilly Media - The authoritative reference for pandas, written by its creator; covers DataFrame operations, time-series resampling, rolling statistics, missing data handling, and CSV/JSON I/O directly applicable to hydroponic data pipelines.

  5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - Aurélien Géron - O'Reilly Media - Covers linear regression, anomaly detection, Z-score normalization, and scikit-learn API patterns applicable to building predictive models for hydroponic yield and nutrient drift detection.

  6. pandas Documentation - pandas.pydata.org - Complete API reference for pandas including DataFrame, Series, time-series resampling, rolling windows, merge/join, and groupby operations used throughout hydroponic data analysis workflows.

  7. NumPy Documentation - numpy.org - Official reference for NumPy array operations, statistical functions (mean, std, percentile), and linear algebra routines used for rolling window calculations and correlation analysis on sensor data.

  8. Real Python: pandas Tutorial - Real Python - Comprehensive tutorial on creating, indexing, filtering, and transforming pandas DataFrames with real data examples; directly applicable to working with CSV sensor logs from hydroponic monitoring systems.

  9. Python Docs: csv Module - Python.org - Official documentation for the CSV reader and writer module used to parse sensor log files written by MicroPython nodes and load them into pandas DataFrames for analysis.

  10. MIT OCW: Statistics for Applications - MIT OpenCourseWare - Graduate statistics course covering confidence intervals, hypothesis testing, regression, and anomaly detection methods applicable to validating hydroponic sensor data and detecting crop health problems.