Hybrid Plant Growth: Integrating Stochastic, Empirical, and Optimization Models with Machine Learning for Controlled Environment Agriculture
Controlled Environment Agriculture (CEA) offers a viable solution for sustainable crop production, yet the optimization of the latter requires precise modeling and resource management. This study introduces a novel hybrid plant growth model integrating stochastic, empirical, and optimization approac...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/1/189 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589384934752256 |
|---|---|
| author | Nezha Kharraz István Szabó |
| author_facet | Nezha Kharraz István Szabó |
| author_sort | Nezha Kharraz |
| collection | DOAJ |
| description | Controlled Environment Agriculture (CEA) offers a viable solution for sustainable crop production, yet the optimization of the latter requires precise modeling and resource management. This 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, CO<sub>2</sub>, 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 cm<sup>2</sup>), 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. |
| format | Article |
| id | doaj-art-277942028fa44dffadd9c7a1f4dd0949 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-277942028fa44dffadd9c7a1f4dd09492025-01-24T13:17:06ZengMDPI AGAgronomy2073-43952025-01-0115118910.3390/agronomy15010189Hybrid Plant Growth: Integrating Stochastic, Empirical, and Optimization Models with Machine Learning for Controlled Environment AgricultureNezha Kharraz0István Szabó1Doctoral School of Mechanical Engineering, Hungarian University of Agriculture and Life Sciences (MATE), Páter K. 1, H-2100 Gödöllő, HungaryInstitute of Technology, Hungarian University of Agriculture and Life Science (MATE), Páter K. 1, H-2100 Gödöllő, HungaryControlled Environment Agriculture (CEA) offers a viable solution for sustainable crop production, yet the optimization of the latter requires precise modeling and resource management. This 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, CO<sub>2</sub>, 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 cm<sup>2</sup>), 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.https://www.mdpi.com/2073-4395/15/1/189Controlled Environment Agriculture (CEA)hybrid plant growthGrowth Efficiency Ratio (GER)Plant Growth Index (PGI)optimization algorithmsmachine learning |
| spellingShingle | Nezha Kharraz István Szabó Hybrid Plant Growth: Integrating Stochastic, Empirical, and Optimization Models with Machine Learning for Controlled Environment Agriculture Agronomy Controlled Environment Agriculture (CEA) hybrid plant growth Growth Efficiency Ratio (GER) Plant Growth Index (PGI) optimization algorithms machine learning |
| title | Hybrid Plant Growth: Integrating Stochastic, Empirical, and Optimization Models with Machine Learning for Controlled Environment Agriculture |
| title_full | Hybrid Plant Growth: Integrating Stochastic, Empirical, and Optimization Models with Machine Learning for Controlled Environment Agriculture |
| title_fullStr | Hybrid Plant Growth: Integrating Stochastic, Empirical, and Optimization Models with Machine Learning for Controlled Environment Agriculture |
| title_full_unstemmed | Hybrid Plant Growth: Integrating Stochastic, Empirical, and Optimization Models with Machine Learning for Controlled Environment Agriculture |
| title_short | Hybrid Plant Growth: Integrating Stochastic, Empirical, and Optimization Models with Machine Learning for Controlled Environment Agriculture |
| title_sort | hybrid plant growth integrating stochastic empirical and optimization models with machine learning for controlled environment agriculture |
| topic | Controlled Environment Agriculture (CEA) hybrid plant growth Growth Efficiency Ratio (GER) Plant Growth Index (PGI) optimization algorithms machine learning |
| url | https://www.mdpi.com/2073-4395/15/1/189 |
| work_keys_str_mv | AT nezhakharraz hybridplantgrowthintegratingstochasticempiricalandoptimizationmodelswithmachinelearningforcontrolledenvironmentagriculture AT istvanszabo hybridplantgrowthintegratingstochasticempiricalandoptimizationmodelswithmachinelearningforcontrolledenvironmentagriculture |