Extending logistic growth for smart agriculture: A four-dimensional model integrating photosynthesis and resource efficiency
Year: 2025
Type: article
Abstract: This paper introduces a novel four-dimensional extension of the logistic growth model for smart agriculture, integrating plant height, biomass, chlorophyll content, and leaf area into the existing logistic framework. Unlike traditional models, which focus on single-dimensional traits, this work introduces multidimensional biological traits and dynamically adjusts the carrying capacity (K) and growth rate (r) based on real-time environmental factors such as light intensity (100–600 µmol m−2s−1), temperature (18°C to 28°C), and nutrient availability (Low, Medium, High). The growth rate is refined through a photosynthesis-driven model, incorporating light intensity and chlorophyll content as key variables. A significant innovation is the inclusion of sustainability factors, such as energy savings (20–25%) and CO2 reduction (15%), which collectively reduce energy consumption by 22% while maintaining plant growth efficiency. Simulations over a 90-day growth cycle showed a 15% increase in biomass accumulation under adaptive resource allocation compared to baseline conditions. The model achieves a predictive accuracy of R2 = 0.93 and RMSE = 0.12, validating its effectiveness in simulating real-world conditions. This work represents a significant advancement in the field, offering a new framework for precision agriculture that optimizes crop yields and minimizes resource waste in controlled environments.
Source: Smart Agricultural Technology
Authors: Nezha Kharraz, István Szabó
Institution: Magyar Agrár- és Élettudományi Egyetem
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,770
OpenAlex ID: W4412658271