Optimization of Tinyml Models for Plant Disease Classification Using Pso Algorithm

Keywords:
TinyML, CNN, Particle Swarm Optimization (PSO), Hyperparameter Optimization, Plant Disease Classification.

Abstract:
This paper presents an approach for improving the performance of TinyML models through metaheuristic optimization. Due to limited computational resources in IoT environments, achieving high classification accuracy remains a challenge. The proposed method utilizes Particle Swarm Optimization (PSO) for tuning convolutional neural network (CNN) hyperparameters in the task of plant disease classification. Experimental results indicate that optimized models outperform baseline configurations in terms of accuracy and stability, with an improvement of over 20% in MCC compared to the baseline model. This study represents a part of a broader doctoral research focused on efficient and explainable AI models for resource-constrained environments.

CITATION:

IEEE format

Copy to clipboard

V. Radojčić, M. Dobrojević, N. Bačanin Džakula, M. Živković, L. Jovanović, “Optimization of Tinyml Models for Plant Disease Classification Using Pso Algorithm,” in Sinteza 2025 - International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Singidunum University, Serbia, 2026, pp. 137-143. doi:10.15308/Sinteza-2026-137-143

APA format

Copy to clipboard

Radojčić, V., Dobrojević, M., Bačanin Džakula, N., Živković, M., Jovanović, L. (2026). Optimization of Tinyml Models for Plant Disease Classification Using Pso Algorithm. Paper presented at Sinteza 2025 - International Scientific Conference on Information Technology, Computer Science, and Data Science. doi:10.15308/Sinteza-2026-137-143

BibTeX format
Download

RefWorks Tagged format
Download