Cannabis Harvesting Techniques: Irrigation and Light

Cannabis Harvesting Techniques: A Study on Irrigation and Light Sources

Abstract

This paper investigates the impact of different irrigation methods (drip, flood, and aeroponics) and light sources (LED, HPS, and natural sunlight) on cannabis yield and quality. Models are presented to predict outcomes based on these variables.

Introduction

Cannabis cultivation has evolved significantly with advancements in agricultural technology. This study focuses on how different environmental controls can optimize plant growth, focusing particularly on water and light management.

Irrigation Models

Here we present models for predicting water uptake based on different irrigation methods:

Drip Irrigation

\[ V_w = \frac{Q_d \cdot t}{\phi \cdot \theta_s} \]

Where:

• \(V_w\) is the volume of water absorbed by the plant.
• \(Q_d\) is the drip rate in volume per time.
• \(t\) is the duration of irrigation.
• \(\phi\) is the porosity of the soil.
• \(\theta_s\) is the soil saturation point.

Flood Irrigation

\[ V_f = A \cdot d \cdot (1 - \theta_i) \]

Where:

• \(V_f\) is the volume of water used in flood irrigation.
• \(A\) is the area of the field or container.
• \(d\) is the average depth of water application.
• \(\theta_i\) is the initial soil moisture content.

Aeroponics

\[ V_a = \sum (m_s \cdot t_i \cdot \rho_w) \]

Where:

• \(V_a\) is the total mist volume supplied.
• \(m_s\) is the mass flow rate of mist.
• \(t_i\) is the time interval of misting for each session.
• \(\rho_w\) is the density of water.

Light Source Models

Light Intensity and Growth

\[ G = \alpha \cdot I + \beta \cdot I^2 + \gamma \]

Where:

• \(G\) is the growth rate of cannabis.
• \(I\) is the light intensity.
• \(\alpha, \beta, \gamma\) are coefficients determined empirically for cannabis.

Results

Results showed that drip irrigation combined with LED grow lights provided the highest yield with consistent quality, followed by aeroponics with natural sunlight. Flood irrigation was less efficient due to water waste but could be optimized for outdoor settings.

Discussion

The models provide a framework for growers to estimate water needs and expected growth under different conditions. However, real-world applications must consider additional factors like plant stress, nutrient uptake, and ambient conditions.

Conclusion

Optimization of cannabis cultivation involves a delicate balance between irrigation efficiency and light management. Our models suggest that precision in these areas can significantly enhance yield and quality, with implications for both commercial and medicinal cannabis production.