Indoor Plant Sunlight Analysis System

HydroPhoton – Indoor Plant Sunlight Analysis – User Manual

The HydroPhoton – Indoor Plant Sunlight Analysis System is, at its heart, a very smart web tool for people who grow plants indoors and are tired of guessing. It answers one frustrating question with hard numbers: How much actual, plant-usable sunlight is making it through my window? It’s built for the precision-obsessed—hydroponic growers, urban farmers, the folks who measure their pH to the second decimal. It doesn’t just give you a guess; it crunches geography, architecture, and the physics of light to tell you exactly what your plants are getting. Frankly, it’s the kind of tool I wish I’d had a decade ago.

Table of Contents

1. System Requirements
2. Getting Started
3. Interface Components
4. Input Parameters
5. Output Metrics
6. Analysis Algorithms
7. Interpretation Guidelines
8. Optimization Recommendations
9. Troubleshooting
10. Technical Specifications

1. System Requirements 

Let’s be practical. You don’t need a supercomputer. Any relatively modern web browser will do—Chrome, Firefox, Safari, Edge. Just make sure JavaScript is turned on. You’ll also need an internet connection, mostly for grabbing your location and loading the core libraries.

The geolocation bit is key. Your browser will ask for permission to find you—that’s how it pins down your coordinates. It’s pretty accurate, usually within 10 meters or so. If that creeps you out, you can always punch in your latitude and longitude manually. As for screen size, anything above 1280×720 works, but you’ll thank yourself for a 1080p display. All those charts need room to breathe.

2. Getting Started 

The setup’s straightforward. Open the HTML file in your browser. You’ll be asked about location access—say yes if you want the easy path. Then hit that big button: “Get My Location Automatically.” A few seconds later, you should see your city or coordinates pop up.

If the auto-detect fails—and it sometimes does, especially in dense apartments—just type your latitude and longitude into the boxes. Seattle’s already in there as a default (47.6062, -122.3321), which is a nice touch. The whole workflow boils down to this: set your location, describe your window, describe your room, and let it analyze. Takes about a minute, tops.

3. Interface Components

The layout is a three-panel dashboard, and it’s smartly organized. The Header tells you what you’re working with and shows your location status. Pay attention to that status light—it’ll tell you if your coordinates are locked in or if something’s off.

The Left Panel is where you do all the configuring. It’s your control center for geography, window specs, and room details. The Main Panel is the star of the show: four detailed charts and a monthly data table that break down your light and climate, month by month. Finally, the Right Panel gives you the bottom line—the key metrics, how your chosen crops will fare, and, crucially, a list of recommendations for what to actually do about the results. It’s a logical flow: configure, analyze, interpret.

4. Input Parameters

This is where the magic—or the modeling—starts. You’ve got to feed the beast good data. For Geographic Parameters, it’s your latitude and longitude. This sets the sun’ entire arc for the year. Get this wrong, and everything else is just a pretty guess.

The Window Parameters are critical. Azimuth—the compass direction your window faces—is the big one. 180° is due south (ideal in the Northern Hemisphere). You also set the physical size (height, width), the glazing type (single, double, low-e), and your floor level. These all change the light and heat equation.

Then there are the Room Parameters. Room depth is how far back from the window your plants can sit. Wall reflectivity is a sneaky-important one—a white wall bounces a surprising amount of light. You’ll also check off external obstructions (buildings, trees) and pick your growing goal (are you raising lettuce or tomatoes?). Be honest here. Your results are only as good as your inputs.

5. Output Metrics 

Now, the results. The system spits out a suite of metrics, but you only need to obsess over a few. First is the Daily Light Integral (DLI). This is the total number of plant-usable photons hitting a square meter each day, measured in mol/m²/day. It’s the single best number for judging if you have enough light. For tomatoes, you want at least 15, ideally 20+. For lettuce, you can get by with 8-12.

Next is your Indoor Temperature Range. This is a predicted min and max at plant height, in °C. You’re looking for a stable band, ideally between 18 and 26°C for most crops. Big swings stress plants out.

Usable Sunlight Hours tells you how many hours a day the light through your window is strong enough to actually drive growth (above 100 µmol/m²/s). Six or more is solid for fruiting plants.

Then you get the UISE Volume (Usable Indoor Sunlight Envelope), in cubic meters. Think of it as the 3D bubble of space in your room where the light is good enough to grow. A bigger number means more viable growing real estate.

Finally, the ITSI Score (Indoor Thermal Stability Index), on a 0-100 scale. It judges how stable your temps are. Higher is better. 80+ is excellent; below 60 means your plants are riding a rollercoaster.

6. Indoor Plant Sunlight Analysis Algorithms 

Alright, let’s peek under the hood. How does it get these numbers? It’s a cascade of calculations. It starts with the SunCalc.js library to find the sun’s exact position in the sky for your location and time. Then it calculates the Outdoor PPFD (Photosynthetic Photon Flux Density)—the raw photon stream before it hits your window.

The real modeling happens at the window. The tool calculates a window efficiency score. It factors in the angle of the sun hitting the glass, the glazing type (double-pane blocks more than single), and even reflections off the surface. It’s not just a simple percentage—it’s trigonometry and material science.

That attenuated light then travels into your room. The Indoor PPFD calculation accounts for how light fades with distance from window (it decays exponentially) and gets a boost from wall reflectivity. All these PPFD values, sampled across the entire day, are integrated to produce your final DLI.

The temperature modeling is a separate but linked beast. It uses your latitude to establish a seasonal baseline, then modifies it based on window direction (south-facing gets more solar heat gain) and glazing insulation. It’s an estimate, sure, but it’s built on solid climatic principles.

7. Interpretation Guidelines

So you’ve got a bunch of numbers. What do they mean for your plants? Let’s translate.

For DLI, here’s a quick cheat sheet:

• Below 8 mol/m²/day: You’re in the basement. Only the hardiest low-light greens (like some lettuce) will survive without help.
• 8-12: The marginal zone. Herbs and leafy greens will grow, but slowly. Fruiting is off the table.
• 12-18: Now we’re talking. This is good light for herbs and excellent for greens. Some dwarf fruiting varieties might work.
• 18-25: The sweet spot for tomatoes, peppers, cucumbers. Robust growth and good yields.
• Above 25: A lot of light. Great for heat-loving crops, but watch for signs of stress on tender leaves.

Temperature is simpler. Below 10°C, growth stops. Between 15-25°C is the productivity zone. Above 32°C, many plants start to shut down—pollination fails, leaves scorch. Your goal is to keep the needle in that middle band as much as possible.

Think seasonally, too. Winter is about light scarcity and cold drafts. Your strategy is maximization and protection. Spring is often the goldilocks period. Summer brings too much of a good thing—heat and sometimes excessive light. Fall is a race against declining light. Your crop choices should dance to this rhythm.

8. Optimization Recommendations 

The tool doesn’t just diagnose; it prescribes. The recommendations panel is where you find your to-do list.

If your DLI is low, it’ll talk supplemental lighting. It might say: “You’ve got a 5 mol/m²/day gap. To fix it, add a medium-intensity LED panel providing 150 µmol/m²/s for 8 hours a day.” That’s actionable intel. It’ll even suggest fixture types and the power you’ll draw.

It’s big on reflective surfaces. A flat white wall has 70-80% reflectivity. Paint it bright white or tack up some Mylar, and you bump that to 90%+. That’s a free 15-25% boost to your effective light levels—no electricity required. It’s the easiest win in indoor gardening.

For temperature issues, the advice gets practical. Too cold? Thermal curtains at night, and maybe a small space heater on a thermostat. Too hot? Apply a shading film to the window, set up a fan for airflow, and pull your plants back from the glass a foot or two. It’s about working with your space, not against it.

9. Troubleshooting – Indoor Plant Sunlight Analysis

Look, models aren’t perfect. If your numbers seem wildly off—like a DLI of 30 in a north-facing window in December—something’s wrong. First, check your latitude and longitude. A misplaced decimal point can throw you into a different hemisphere.

Did you allow geolocation? If not, and you didn’t manually enter coordinates, it’s calculating for the default (Seattle). That’s your most common “weird result” culprit.

The temperature predictions are based on averages. If you live in a uniquely drafty old building or a super-insulated new one, your real-world temps will deviate. Use the prediction as a baseline, then adjust with a cheap thermometer. The model’s strength is in showing you the pattern—the summer spike, the winter dip—more than the absolute nightly low.

And if the charts are blank or frozen, try a hard refresh in your browser (Ctrl+F5 or Cmd+Shift+R). Sometimes the Chart.js library needs a kick.

10. Technical Specifications

For the technically curious, here’s the scaffolding. The core light calculation assumes a standard clear sky—no clouds. It uses a PAR Constant of about 2000 µmol/m²/s for peak sun and an Air Mass Model to account for atmospheric scattering. The DLI is calculated using Simpson’s Rule for integration, sampling light levels at 30-minute intervals across the day.

It leans on two main libraries: Chart.js for the visualizations and SunCalc.js for the solar positioning, which is remarkably precise. The Nominatim API from OpenStreetMap is what turns your coordinates into a city name.

A few key assumptions are baked in: your window is reasonably clean (92% transmission factor), about 85% of the frame is actually glass, and your room is a simple rectangle. It’s a model of a typical scenario, not a quantum-level simulation. It’s designed to be right enough to make excellent decisions, which, in my book, is what matters.

This tool gives you a powerful, physics-based forecast. But your plants are the ultimate sensor. Use this analysis to guide your setup, but always watch how your actual crops respond. Tweak. Adapt. That’s what makes a great grower. Now go get your hands dirty.