Szczegóły publikacji

Opis bibliograficzny

Explaining multi-objective multispectral models for soil parameters’ retrieval / Jakub Mielcarek, Katarzyna Rudnik, Adriana NIEPALA, Mina Bikhit, Jakub Nalepa, Bogdan Ruszczak // W: Explainable AI in space : second international workshop, EASi 2026 held in conjunction with the 35th International Joint Conference on Artificial Intelligence, IJCAI-ECAI 2026 : Bremen, Germany, August 15–21, 2026 : proceedings / eds. Jakub Nalepa, [et al.]. — Switzerland : Springer, cop. 2027. — ( Communications in Computer and Information Science ; ISSN 1865-0929 ; vol. 3107 ). — ISBN: 978-3-032-36805-8; e-ISBN: 978-3-032-36806-5. — S. 27–45. — Bibliogr., Abstr. — Publikacja dostępna online od: 2026-08-21. — A. Niepala – dod. afiliacja: Opole University of Technology, Opole, Poland

Autorzy (6)

Mielcarek Jakub
Rudnik Katarzyna
AGHNiepala Adriana
Bikhit Mina
Nalepa Jakub
Ruszczak Bogdan

Słowa kluczowe

non-dominated sorting genetic algorithm explainable artificial intelligence soil parameters’ estimation machine learning remote sensing precision agriculture

Dane bibliometryczne

ID BaDAP	170617
Data dodania do BaDAP	2026-10-16
DOI	10.1007/978-3-032-36806-5_3
Rok publikacji	2027
Typ publikacji	materiały konferencyjne (aut.)
Otwarty dostęp
Wydawca	Springer
Konferencja	International Joint Conference on Artificial Intelligence 2026
Czasopismo/seria	Communications in Computer and Information Science

Abstract

Estimating soil biophysical parameters from multispectral imagery is essential for precision agriculture, yet deploying accurate machine learning pipelines presents significant computational challenges. Individual soil trace elements often require specific model configurations, making traditional exhaustive hyperparameter optimization methods prohibitively time-consuming for resource-constrained embedded systems. In this study, we present a rigorous comparative analysis of two hyperparameter optimization strategies for multi-target soil estimation: the conventional, element-specific grid search, and the multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA-II). Moreover, three explainable artificial intelligence methods were employed to analyze the behavior of the developed models and to provide insights into the contribution of input features to their predictions.