1. What can artificial intelligence do for soil health in agriculture?Stefan Schweng, Luca Bernardini, Katharina Keiblinger, Peter Kaul, Iztok Fister, Niko Lukač, Javier Del Ser, Andreas Holzinger, 2025, pregledni znanstveni članek Opis: The integration of artificial intelligence (AI) into soil research presents significant opportunities to advance the understanding, management, and conservation of soil ecosystems. This paper reviews the diverse applications of AI in soil health assessment, predictive modeling of soil properties, and the development of pedotransfer functions within the context of agriculture, emphasizing AI’s advantages over traditional analytical methods. We identify soil organic matter decline, compaction, and biodiversity loss as the most frequently addressed forms of soil degradation. Strong trends include the creation of digital soil maps, particularly for soil organic carbon and chemical properties using remote sensing or easily measurable proxies, as well as the development of decision support systems for crop rotation planning and IoT-based monitoring of soil health and crop performance. While random forest models dominate, support vector machines and neural networks are also widely applied for soil parameter modeling. Our analysis of datasets reveals clear regional biases, with tropical, arid, mild continental, and polar tundra climates remaining underrepresented despite their agricultural relevance. We also highlight gaps in predictor–response combinations for soil property modeling, pointing to promising research avenues such as estimating heavy metal content from soil mineral nitrogen content, microbial biomass, or earthworm abundance. Finally, we provide practical guidelines on data preparation, feature extraction, and model selection. Overall, this study synthesizes recent advances, identifies methodological limitations, and outlines a roadmap for future research, underscoring AI’s transformative potential in soil science.
Ključne besede: artificial intelligence, machine learning, agriculture, soil health, soil parameter modeling, regional data bias
Objavljeno v DKUM: 17.10.2025; Ogledov: 0; Prenosov: 3
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2. LLM in the loop: a framework for contextualizing counterfactual segment perturbations in point cloudsVeljka Kočić, Niko Lukač, Dzemail Rozajac, Stefan Schweng, Christoph Gollob, Arne Nothdurft, Karl Stampfer, Javier Del Ser, Andreas Holzinger, 2025, izvirni znanstveni članek Opis: Point Cloud Data analysis has seen a major leap forward with the introduction of PointNet algorithms, revolutionizing how we process 3D environments. Yet, despite these advancements, key challenges remain, particularly in optimizing segment perturbations to influence model outcomes in a controlled and meaningful way. Traditional methods struggle to generate realistic and contextually appropriate perturbations, limiting their effectiveness in critical applications like autonomous systems and urban planning. This paper takes a bold step by integrating Large Language Models into the counterfactual reasoning process, unlocking a new level of automation and intelligence in segment perturbation. Our approach begins with semantic segmentation, after which LLMs intelligently select optimal replacement segments based on features such as class label, color, area, and height. By leveraging the reasoning capabilities of LLMs, we generate perturbations that are not only computationally efficient but also semantically meaningful. The proposed framework undergoes rigorous evaluation, combining human inspection of LLM-generated suggestions with quantitative analysis of semantic classification model performance across different LLM variants. By bridging the gap between geometric transformations and high-level semantic reasoning, this research redefines how we approach perturbation generation in Point Cloud Data analysis. The results pave the way for more interpretable, adaptable, and intelligent AI-driven solutions, bringing us closer to realworld applications where explainability and robustness are paramount.
Ključne besede: explainable AI, point cloud data, counterfactual reasoning, LiDAR, 3D point cloud data, interpretability, human-centered AI, large language models, K-nearest neighbors
Objavljeno v DKUM: 19.05.2025; Ogledov: 0; Prenosov: 3
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3. Enhancing trust in automated 3D point cloud data interpretation through explainable counterfactualsAndreas Holzinger, Niko Lukač, Dzemail Rozajac, Emil Johnston, Veljka Kočić, Bernhard Hoerl, Christoph Gollob, Arne Nothdurft, Karl Stampfer, Stefan Schweng, Javier Del Ser, 2025, izvirni znanstveni članek Opis: This paper introduces a novel framework for augmenting explainability in the interpretation of point cloud data by fusing expert knowledge with counterfactual reasoning. Given the complexity and voluminous nature of point cloud datasets, derived predominantly from LiDAR and 3D scanning technologies, achieving interpretability remains a significant challenge, particularly in smart cities, smart agriculture, and smart forestry. This research posits that integrating expert knowledge with counterfactual explanations – speculative scenarios illustrating how altering input data points could lead to different outcomes – can significantly reduce the opacity of deep learning models processing point cloud data. The proposed optimization-driven framework utilizes expert-informed ad-hoc perturbation techniques to generate meaningful counterfactual scenarios when employing state-of-the-art deep learning architectures. The optimization process minimizes a multi-criteria objective comprising counterfactual metrics such as similarity, validity, and sparsity, which are specifically tailored for point cloud datasets. These metrics provide a quantitative lens for evaluating the interpretability of the counterfactuals. Furthermore, the proposed framework allows for the definition of explicit interpretable counterfactual perturbations at its core, thereby involving the audience of the model in the counterfactual generation pipeline and ultimately, improving their overall trust in the process. Results demonstrate a notable improvement in both the interpretability of the model’s decisions and the actionable insights delivered to end-users. Additionally, the study explores the role of counterfactual reasoning, coupled with expert input, in enhancing trustworthiness and enabling human-in-the-loop decision-making processes. By bridging the gap between complex data interpretations and user comprehension, this research advances the field of explainable AI, contributing to the development of transparent, accountable, and human-centered artificial intelligence systems.
Ključne besede: explainable AI, point cloud data, counterfactual reasoning, information fusion, interpretability, human-centered AI
Objavljeno v DKUM: 06.03.2025; Ogledov: 0; Prenosov: 6
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