context/1_purpose.md

Photosynthesis Prediction Model – Purpose & Background

This project builds a two-stage photosynthesis prediction pipeline for grapevines:

• Stage 1 (Mechanistic): Use on-site crop sensor data from the Seymour plot to compute leaf photosynthesis rate (A) using the Farquhar et al. (1980) model with Greer & Weedon (2012) parameterization for Vitis vinifera. This includes temperature-dependent Vcmax/Jmax, electron transport, and a Crop Water Stress Index (CWSI) based on leaf–air temperature differences and humidity.
• Stage 2 (ML): Train simple machine‑learning models to predict (A) using only external IMS weather station data (station 43, Sde Boker). This lets us estimate grapevine photosynthesis from standard meteorological inputs without needing the full sensor stack.

Domain context

• Site: solar wine‑farm experimental vineyard near Yeruham, Israel (Seymour plot).
• Crop: grapevine (Vitis vinifera).
• On‑site sensors: PAR, leaf temperature, air temperature, CO₂, VPD, humidity, and crop spectral indices (PRI, NDVI variants).
• External weather: IMS station 43 (Sde Boker) providing temperature, solar radiation, humidity, rain, wind, and pressure.

Design principles

• Mechanistic first, then ML: Ground the target (A) in a physiological model before fitting data‑driven models.
• Strict separation of data sources: Stage 1 uses only on‑site sensors; Stage 2 uses only IMS weather features to avoid leakage.
• Reproducible, well‑documented pipeline: Clear directory layout, config in config/settings.py, and this context/ folder as the long‑term project memory.