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DOI: 10.15507/2658-4123.035.202504.678-699

UDK 004.89:547.979.7

Estimating Chlorophyll Content by Optical Density of Plant Leaves Using Machine Learning

Sergei A. Rakutko
Dr.Sci (Eng.), Chief Researcher of the Department of Agroecology in Livestock Production, Institute for Engineering and Environmental Problems in Agricultural Production (IEEP) – branch of Federal Scientific Agroengineering Center VIM (3 Filtrovskoje Shosse, Tiarlevo, St. Petersburg 196634, Russian Federation), ORCID: https://orcid.org/0000-0002-2454-4534, Researcher ID: B-2745-2014, This email address is being protected from spambots. You need JavaScript enabled to view it.

Yelena N. Rakutko
Researcher, Department of Agroecology in Livestock Production, Institute for Engineering and Environmental Problems in Agricultural Production (IEEP) – branch of Federal Scientific Agroengineering Center VIM (3 Filtrovskoje Shosse, Tiarlevo, St. Petersburg, 196634, Russian Federation), ORCID: https://orcid.org/0000-0002-3536-9639, This email address is being protected from spambots. You need JavaScript enabled to view it.

Jian Su
Cand.Sci (Eng.), Senior Engineer, Research Institute of Agricultural Equipment, Academy of Agricultural Sciences, Xinjiang Uygur Autonomous Region (291 Nanchannan St., Urumqi 830091, People's Republic of China), ORCID: https://orcid.org/0000-0001-5120-3623, SPIN-code: 6210-611, This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract
Introduction. Chlorophyll plays a crucial role in absorbing and transforming light energy into a chemical form that provides organic matter production in plants. Monitoring of chlorophyll content helps to assess plant-environment interactions and the degree of influence of stress factors that are essential for yield management. Traditional laboratory methods of analyzing are time-consuming, destroying samples and unsuitable for rapid field evaluations. A more reasonable solution is to use low-cost, portable devices.
Aim of the Study. The study is aimed at developing and training an ANN architecture to predict the chlorophyll content in plant leaves based on their optical density within specific visible spectrum ranges.
Materials and Methods. The artificial neural network dataset was compiled from experimental measurements using the DP-1M densitometer and the CCM-200 chlorophyll meter. Data were collected from lettuce, pepper, tomato and zucchini leaves of different ages, which were grown in different light environments. The artificial neural network training was carried out in the Google Colab environment with subsequent adaptation of the model for using in a microcontroller device – a photocolorimeter for leaves.
Results. The dataset with 1,000 entries showed that the leaf optical density range is from 0.57 to 2.54 relative units (red), from 0.9 to 1.66 relative units (green), and from 1.09 to 3.53 relative units (blue). According to these data, the chlorophyll content variations are from 3.1 to 156.5 relative units. In the study, there were compared six artificial neural network architectures that differed by hidden-layer neurons. The structure “32:32” had the highest accuracy (MAE = 6.64 rel. units, MAPE = 16.34%, R² = 0.8886). A simplified structure “4:4” was selected to simplify the model and improve the microcontroller efficiency. This structure maintained the performance (MAE = 6.83 rel. units, MAPE = 16.86%, R² = 0.8808) with much smaller amount of resources used – 41 weight parameters and 164 bytes of memory. A comparative evaluation with classical machine learning algorithms demonstrated the superiority of the developed model across all metrics.
Discussion and Conclusion. The trained artificial neural network was implemented on a microcontroller-based photocolorimeter for leaves that enabled the non-destroying optical density measurements. The developed model allows implementing non-destroying and operational monitoring of the condition of plants, which is especially important in precision farming systems. This approach has significant potential for ecological monitoring and precision agriculture. The study results demonstrate the viability of machine learning for improving plant status assessment and developing digital agrotechnology solutions.

Keywords: plant lighting, plant leaf, chlorophyll content, optical density, artificial neural network, machine learning

Funding: This work was supported by the Ministry of Science and Higher Education of the Russian Federation under the State Assignment of the Federal cientific Center for VIM (No. FGUN-2025-0010 “Develop energy- and resource-saving machine technologies and digital monitoring and control systems for environmentally friendly agricultural production”, specifically for the creation of a prototype digital environmental monitoring tool), 2025.

Acknowledgments: The authors thank their colleagues: Senior Researcher A. P. Mishanov for their meticulous work in creating conditions for plant growth and assistance with initial measurements; Candidate of Agricultural Sciences A. E. Markova for preparing the experimental material; and the reviewers for their contribution to the peer review of the work.

Conflict of interest: the authors declare that there is no conflict of interest.

For citation: Rakutko S.A., Rakutko Ye.N., Su J. Estimating Chlorophyll Content by Optical Density of Plant Leaves Using Machine Learning. Engineering Technologies and Systems. 2025;35(4):678–699. https://doi.org/10.15507/2658-4123.035.202504.678-699

Authors contribution:
S. A. Rakutko – formulating the study ideas, objectives and aims. preparing the manuscript, specifically visualizing the study results and data obtained.
E. N. Rakutko – using statistical, mathematical, computational or other formal techniques to analyze or synthesize the study data; conducting a research and investigation process, specifically performing the experiments and data collection.
J. Su – creating the computer code and implementing supporting algorithms, preparing and presenting the manuscript, specifically writing the initial manuscript draft (including the translation of the manuscript into the English language).

All authors have read and approved the final manuscript.

Submitted 17.06.25;
revised 25.09.2025;
accepted 08.10.2025

REFERENCES

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