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DOI: 10.15507/2658-4123.036.202601.114-139

UDK 004.932.2:631.17:581.2:633.11

 

Development of a Low-Power Onboard System for Point Detection of Wheat Diseases Using a Modified YOLO Architecture

 

Salavat G. Mudarisov
Dr.Sci (Eng.), Professor, Academician of the Academy of Sciences of the Republic of Bashkortostan, leading researcher at the Laboratory of Digital Twins and Machine Design for Chemical and Biological Plant Protection, Bashkir State Agrarian University (34 50-letiya Oktyabrya St., Ufa 450001, Russian Federation), ORCID: https://orcid.org/0000-0001-9344-2606, Scopus ID: 57200284613, Researcher ID: G-2217-2018, This email address is being protected from spambots. You need JavaScript enabled to view it.

Ilnur R. Miftakhov
Cand.Sci (Eng.), Researcher, Bashkir State Agrarian University (34 50-letiya Oktyabrya St., Ufa 450001, Russian Federation), ORCID: https://orcid.org/0000-0002-3125-3532, Scopus ID: 57204635364, Researcher ID: JPX-2370-2023, This email address is being protected from spambots. You need JavaScript enabled to view it.

Ildar M. Farkhutdinov
Dr.Sci (Eng.), Associate Professor of the Department of Mechatronic Systems and Machines of Agricultural Production, Bashkir State Agrarian University (34 50-letiya Oktyabrya St., Ufa 450001, Russian Federation), ORCID: https://orcid.org/0000-0002-6443-8584, Researcher ID: G-2816-2018, This email address is being protected from spambots. You need JavaScript enabled to view it.

Elina I. Shafeeva
Cand.Sci (Agr.), Associate Professor, Bashkir State Agrarian University (34 50-letiya Oktyabrya St., Ufa 450001, Russian Federation), ORCID: https://orcid.org/0000-0003-2535-1520, This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Abstract
Introduction. Early diagnosis of diseases of cereal crops is a key task for precision agriculture, because late disease detection results in significant yield losses and inefficient use of plant protection products. Existing phytosanitary control systems mainly focus on offline data post-processing and require substantial computational resources that limits their use on board unmanned aerial vehicles (UAV). In this context, a relevant scientific challenge is to ensure highly accurate real-time plant disease diagnosis under strict computational and energy constraints of onboard platforms.
Aim of the Study. The study is aimed at developing an energy-efficient onboard wheat disease detection system capable of real-time operation on embedded platforms.
Materials and Methods. The object of the study was wheat crops under open-field conditions. The system was tested on the computer modules Jetson TX2, NavQ Plus and Raspberry Pi 4. To build the system, there was used a modified YOLO-based architecture integrating lightweight convolutional blocks (GhostConv, MBConv), attention modules (SE, CBAM), and an extended BiFPN feature aggregation structure. Training was based on a dataset of 7,500 annotated images of brown and yellow rust symptoms.
Results. The developed model demonstrated high detection quality scores: F1-score up to 0.978 and average IoU of 0.82. Performance achieved 16.8 FPS on Jetson TX2 and 13.6 FPS on NavQ Plus, with energy efficiency up to 2.7 FPS/W.
Discussion and Conclusion. The comparative analysis showed that the proposed model outperformed the baseline YOLOv5s according to all key metrics. The proposed architecture demonstrates high accuracy, robustness to noise, and real-time applicability. It can be used to create intelligent crop health control systems and automated plant protection control based on UAV platforms.

Keywords: unmanned aerial vehicle, phytopathology, deep learning, plant disease detection, YOLO, embedded platforms, onboard data processing, precision agriculture, neural network architectures, crop control

Funding: The materials presented in the article were obtained as part of the implementation of the Bashkir State University strategic academic leadership program «Priority 2030».

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

For citation: Mudarisov S.G., Miftakhov I.R., Farkhutdinov I.M., Shafeeva E.I. Development of a Low-Power Onboard System for Point Detection of Wheat Diseases Using a Modified YOLO Architecture. Engineering Technologies and Systems. 2026;36(1):114–139. https://doi.org/10.15507/2658-4123.036.202601.114-139

Authors contribution:
S. G. Mudarisov – developing the study concept and methodology, supervising and managing the study.
I. R. Miftakhov – developing the study concept and methodology, developing the software development, conducting the study, collecting data, formal analysis, visualizing the study results visualization, creating the manuscript draft.
I. M. Farkhutdinov – formal analysis, validating the study results, developing the study methodology, editing and reviewing the manuscript.
E. I. Shafeeva – conducting the study, collecting the data, validating the study results, and resource.

All authors have read and approved the final manuscript.

Submitted 23.07.2025;
revised 07.10.2025;
accepted 20.10.2025

 

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