Sign up for our daily Newsletter and stay up to date with all the latest news!

Subscribe I am already a subscriber

You are using software which is blocking our advertisements (adblocker).

As we provide the news for free, we are relying on revenues from our banners. So please disable your adblocker and reload the page to continue using this site.
Thanks!

Click here for a guide on disabling your adblocker.

Sign up for our daily Newsletter and stay up to date with all the latest news!

Subscribe I am already a subscriber

Optimizing CEA models by using machine learning

Controlled Environment Agriculture (CEA) offers a viable solution for sustainable crop production, yet the optimization of the latter requires precise modeling and resource management.

A recent study introduces a novel hybrid plant growth model integrating stochastic, empirical, and optimization approaches, using Internet of Things sensors for real-time data collection. Unlike traditional methods, the hybrid model systematically captures environmental variability, simulates plant growth dynamics, and optimizes resource inputs.

The prototype growth chamber, equipped with IoT sensors for monitoring environmental parameters such as light intensity, temperature, CO2, humidity, and water intake, was primarily used to provide accurate input data for the model and specifically light intensity, water intake and nutrient intake. While experimental tests on lettuce were conducted to validate initial environmental conditions, this study was focused on simulation-based analysis. Specific tests simulated plant responses to varying levels of light, water, and nutrients, enabling the validation of the proposed hybrid model.

"We varied light durations between 6 and 14 h/day, watering levels between 5 and 10 L/day, and nutrient concentrations between 3 and 11 g/day. Additional simulations modeled different sowing intervals to capture internal plant variability."

The results demonstrated that the optimal growth conditions were 14 h/day of light, 9 L/day of water, and 5 g/day of nutrients; maximized plant biomass (200 g), leaf area (800 cm2), and height (90 cm). Key novel metrics developed in this study, the Growth Efficiency Ratio (GER) and Plant Growth Index (PGI), provided solid tools for evaluating plant performance and resource efficiency.

Simulations showed that GER peaked at 0.6 for approximately 200 units of combined inputs, beyond which diminishing returns were observed. PGI increased to 0.8 to day 20 and saturated to 1 by day 30. The role of IoT sensors was critical in enhancing model accuracy and replicability by supplying real-time data on environmental variability.

The hybrid model's adaptability in the future may offer scalability to diverse crop types and environmental settings, establishing a foundation for its integration into decision-support systems for large-scale indoor farming.

Kharraz, N.; Szabó, I. Hybrid Plant Growth: Integrating Stochastic, Empirical, and Optimization Models with Machine Learning for Controlled Environment Agriculture. Agronomy 2025, 15, 189. https://doi.org/10.3390/agronomy15010189

Source: MDPI

Publication date: