2026-08-08T12:24:01.944Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:12129 2026-08-02T22:48:38Z 1

Optimization of Tinyml Models for Plant Disease Classification Using Pso Algorithm 2026 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/12129 https://portal.sinteza.singidunum.ac.rs/paper/1105 V. Radojčić M. Dobrojević N. Bačanin Džakula M. Živković L. Jovanović 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. conferenceObject 137 143 10.15308/Sinteza-2026-137-143 Sinteza 2026 - International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Serbia