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A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale

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Artificial Intelligence in Geosciences 2025年第1期6卷

作者： Meghraoui, Khadija Racharak, Teeradaj Ait El Kadi, Kenza Bensiali, Saloua Sebari, Imane Research Unit of Geospatial Technologies for A Smart Decision. IAV Hassan II Rabat10101 Morocco Informatics Tohoku University Miyagi Japan School of Information Science Japan Advanced Institute of Science and Technology Ishikawa Japan School of Geomatics and Surveying Engineering. IAV Hassan II Rabat10101 Morocco Department of Applied Statistics and Computer Science. IAV Hassan II Rabat10101 Morocco

Crop-yield is a crucial metric in agriculture, essential for effective sector management and improving the overall production process. This indicator is heavily influenced by numerous environmental factors, particularly those related to soil and climate, which present a challenging task due to the complex interactions involved. In this paper, we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction. This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods, specifically representation learning techniques, along with predictions derived from remote sensing imagery. We tested our proposed methodology on a public dataset centered on corn, aiming to predict crop-yield. Our developed smart model achieved promising results in terms of crop-yield prediction, with a root mean squared error (RMSE) of 1.72, outperforming the baseline models. The ontology-based approach achieved an RMSE of 1.73, while the remote sensing-based method yielded an RMSE of 1.77. This confirms the superior performance of our proposed approach over those using single modalities. This integrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence (AI) represents a significant advancement in agricultural applications. It is particularly effective for crop-yield prediction at the field scale, thus facilitating more informed decision-making in advanced agricultural practices. Additionally, it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy. © 2025 The Authors

关键词： Mean square error

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Procedia computer science 2020年 170卷 59-66页

作者： A. Boulmakoul Z. Fazekas L. Karim P. Gáspár G. Cherradi Computer Sc. Dept. FSTM Hassan II University of Casablanca Morocco Institute for Computer Science and Control (SZTAKI) Budapest Hungary LISA Lab. ENSA Berrechid Hassan I University Morocco

The road environment recognition using embedded technologies has been researched intensively recently. Several papers deal with the detection of the urban road environment-type (RET), such as downtown, residential area, and business/industrial area. These RETs can characterize the road environment around an ego-car. A RET detection approach taking into account relevant traffic signs (TSs) that are visible from the ego-car – along its route – was also proposed. It was assumed that the TS data, namely the type and the location of each detected TS along the route, was made available for the purpose by an on-board TS recognition system (TSR). The TS data is constantly updated, aggregated and evaluated in a multi-scale manner by a RET detection system, so one can produce a probability series of occurrence of each TS type with respect to each of the considered urban RETs. In the present paper, we develop a heuristic for dynamic RET detection using fuzzy similarities on a special graph. We also propose the generic process of this approach, which will be the subject of further development and testing.

关键词： Smart mobility smart road vehicles road environment-type detection fuzzy set theory reactive systems machine learning

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Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson's disease diagnosis

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NeuroImage 2025年 314卷 121256-121256页

作者： Qin, Jianmei Wu, Haoting Wu, Chenqing Guo, Tao Zhou, Cheng Duanmu, Xiaojie Tan, Sijia Wen, Jiaqi Zheng, Qianshi Yuan, Weijin Zhu, Zihao Chen, Jingwen Wu, Jingjing He, Chenyu Ma, Yiran Liu, Chunlei Xu, Xiaojun Guan, Xiaojun Zhang, Minming Department of Radiology the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China Joint Laboratory of Clinical Radiology the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China State Key Laboratory of Computer-aided Design & Computer Graphics Zhejiang University College of Computer Science and technology Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University College of Control Science and Engineering Hangzhou China Department of Electrical Engineering and Computer Sciences University of California Berkeley CA United States Helen Wills Neuroscience Institute University of California Berkeley CA United States

Previous resting state functional MRI (rs-fMRI) analyses of the basal ganglia in Parkinson's disease heavily relied on T1-weighted imaging (T1WI) atlases. However, subcortical structures are characterized by subtle contrast differences, making their accurate delineation challenging on T1WI. In this study, we aimed to introduce and validate a method that incorporates quantitative susceptibility mapping (QSM) into the rs-fMRI analytical pipeline to achieve precise subcortical nuclei segmentation and improve the stability of RSFC measurements in Parkinson's disease. A total of 321 participants (148 patients with Parkinson's Disease and 173 normal controls) were enrolled. We performed cross-modal registration at the individual level for rs-fMRI to QSM (FUNC2QSM) and T1WI (FUNC2T1), *** consistency and accuracy of resting state functional connectivity (RSFC) measurements in two registration approaches were assessed by intraclass correlation coefficient and mutual information. Bootstrap analysis was performed to validate the stability of the RSFC differences between Parkinson's disease and normal controls. RSFC-based machine learning models were constructed for Parkinson's disease classification, using optimized hyperparameters (RandomizedSearchCV with 5-fold cross-validation). The consistency of RSFC measurements between the two registration methods was poor, whereas the QSM-guided approach showed better mutual information values, suggesting higher registration accuracy. The disruptions of RSFC identified with the QSM-guided approach were more stable and reliable, as confirmed by bootstrap analysis. In classification models, the QSM-guided method consistently outperformed the T1WI-guided method, achieving higher test-set ROC-AUC values (FUNC2QSM: 0.87–0.90, FUNC2T1: 0.67–0.70). The QSM-guided approach effectively enhanced the accuracy of subcortical segmentation and the stability of RSFC measurement, thus facilitating future biomarker development in Parkinso

关键词： Parkinson's disease Quantitative susceptibility mapping Resting-state functional connectivity Subcortical nuclei

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Total Variation-Based Image Decomposition and Denoising for Microscopy Images

arXiv

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arXiv 2025年

作者： Corrias, Marco Franceschi, Giada Riva, Michele Tampieri, Alberto Föttinger, Karin Diebold, Ulrike Pock, Thomas Franchini, Cesare Faculty of Physics Center for Computational Materials Science University of Vienna Austria Vienna Doctoral School in Physics University of Vienna Austria Institute of Applied Physics TU Wien Austria Institute of Materials Chemistry TU Wien Austria Institute of Computer Graphics and Vision TU Graz Austria Department of Physics and Astronomy University of Bologna Italy

Experimentally acquired microscopy images are unavoidably affected by the presence of noise and other unwanted signals, which degrade their quality and might hide relevant features. With the recent increase in image acquisition rate, modern denoising and restoration solutions become necessary. This study focuses on image decomposition and denoising of microscopy images through a workflow based on total variation (TV), addressing images obtained from various microscopy techniques, including atomic force microscopy (AFM), scanning tunneling microscopy (STM), and scanning electron microscopy (SEM). Our approach consists in restoring an image by extracting its unwanted signal components and subtracting them from the raw one, or by denoising it. We evaluate the performance of TV-L1, Huber-ROF, and TGV-L1 in achieving this goal in distinct study cases. Huber-ROF proved to be the most flexible one, while TGV-L1 is the most suitable for denoising. Our results suggest a wider applicability of this method in microscopy, restricted not only to STM, AFM, and SEM images. The Python code used for this study is publicly available as part of AiSurf. It is designed to be integrated into experimental workflows for image acquisition or can be used to denoise previously acquired images. © 2025, CC BY.

关键词： Atomic force microscopy

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The First Approach to the Interval Generalized Finite Differences 13th

The First Approach to the Interval Generalized Finite Differ...

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13th International Conference on Parallel Processing and Applied Mathematics, PPAM 2019

作者： Jankowska, Malgorzata A. Marciniak, Andrzej Institute of Applied Mechanics Poznan University of Technology Jana Pawla II 24 Poznan60-965 Poland Institute of Computing Science Poznan University of Technology Piotrowo 2 Poznan60-965 Poland Department of Computer Science State University of Applied Sciences in Kalisz Poznanska 201-205 Kalisz62-800 Poland

ISBN: (纸本)9783030432218

The paper concerns the first approach to interval generalized finite differences. The conventional generalized finite differences are of special interest due to the fact that they can be applied to irregular grids (clouds) of points. Based on these finite differences we can compute approximate values of some derivatives (ordinary or partial). Furthermore, one can formulate the generalized finite difference method for solving some boundary value problems with a complicated boundary of a domain. The aim of this paper is to propose the interval counterparts of generalized finite differences. Under the appropriate assumptions the exact values of the derivatives are included in the interval values obtained. © 2020, Springer Nature Switzerland AG.

关键词： Boundary value problems

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IRIS: Interactive real-time feedback image segmentation with deep learning

IRIS: Interactive real-time feedback image segmentation with...

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Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Pepe, Antonio Schussnig, Richard Li, Jianning Gsaxner, Christina Chen, Xiaojun Fries, Thomas-Peter Egger, Jan Stanford University School of Medicine Department of Radiology 300 Pasteur Dr StanfordCA United States Graz University of Technology Institute of Computer Graphics and Vision Inffeldgasse 16c/II GrazA-8010 Austria Computer Algorithms for Medicine Laboratory GrazA-8010 Austria Graz University of Technology Institute of Structural Analysis Lessingstraße 25/II GrazA-8010 Austria Medical University of Graz Department of Oral and Maxillofacial Surgery Auenbruggerplatz 5/1 GrazA-8036 Austria Shanghai Jiao Tong University School of Mechanical Engineering 800 Dong Chuan Road Shanghai200240 China

ISBN: (纸本)9781510634015

Volumetric examinations of the aorta are nowadays of crucial importance for the management of critical pathologies such as aortic dissection, aortic aneurism, and other pathologies, which affect the morphology of the artery. These examinations usually begin with the acquisition of a Computed Tomography Angiography (CTA) scan from the patient, which is later on postprocessed to reconstruct the 3D geometry of the aorta. The first postprocessing step is referred to as segmentation. Different algorithms have been suggested for the segmentation of the aorta;including interactive methods, as well as fully automatic methods. Interactive methods need to be fine-tuned on each single CTA scan and result in longer duration of the process, whereas fully automatic methods require the possession of a large amount of labeled training data. In this work, we introduce a hybrid approach by combining a deep learning method with a consolidated interaction technique. In particular, we trained a 2D and a 3D U-Net on a limited number of patches extracted from 25 labeled CTA scans. Afterwards, we use an interactive approach, which consists in defining a region of interest (ROI) by just placing a seed point. This seed point is later used as the center of a 2D or 3D patch to be fed to the 2D or 3D U-Net, respectively. Due to the low content variation of these patches, this method allows to correctly segment the ROIs without the need for parameter tuning for each dataset and with a smaller training dataset, requiring the same minimal interaction as state-of-the-art interactive methods. Later on, the new segmented CTA scans can be further used to train a convolutional network for a fully automatic approach. © 2020 SPIE

关键词： Angiography

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Video-based automated emotional monitoring in mental health care supported by a generic patient data management system 2

Video-based automated emotional monitoring in mental health ...

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2nd Symposium of Psychology-Based Technologies, PSYCHOBIT 2020

作者： Hadjar, Hayette Lange, Julian Vu, Binh Felix, Engel Gwendolyn, Mayer Mc Kevitt, Paul Hemmje, Matthias University of Hagen Faculty of Mathematics and Computer Science Hagen Germany Research Institute for Telecommunication and Cooperation Dortmund Germany Heidelberg University Department of Internal Medicine II General Internal Medicine and Psychosomatics Heidelberg Germany Ulster University Derry/Londonderry Ireland

The detection of emotion and expression from video streaming plays a very important role in the mental health care of a patient. The data obtained from it can be used to support the diagnosis of emotional needs related to depression or other kinds of mental illnesses. These data can provide useful emotion monitoring information for health monitoring systems using automatic calculation of this Affective Computing (AC) information and storing them in patient data management systems. This research has been developed in the context of the SenseCare project, in order to support the treatment of patients with primary or comorbid mental disorders. There are two processes for tracking emotion in video, real-time, and offline facial expression video analysis. Real-time video analysis uses streamed webcam videos as data input. Offline video analysis uses pre-recorded video files as input. We focus in this paper on the realtime video analysis process, and we employ deep learning in web browsers for face detection and recognition using JavaScript. © 2020 CEUR-WS. All rights reserved.

关键词： Web browsers

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Multi-Contextual and Multi-Aspect Analysis for Road Surface Type Classification Through Inertial Sensors and Deep Learning

Multi-Contextual and Multi-Aspect Analysis for Road Surface ...

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Brazilian Symposium on Computing System Engineering (SBESC)

作者： Jeferson Menegazzo Aldo von Wangenheim Graduate Program in Computer Science (PPGCC) Image Processing and Computer Graphics Lab (LAPIX) Brazilian National Institute for Digital Convergence (INCoD) Federal University of Santa Catarina (UFSC) Florianópolis Brazil

ISBN: (数字)9781728182865

ISBN: (纸本)9781728182872

The demand for a variety of situational data from the traffic environment and its participants has intensified with the development of applications in Intelligent Transport Systems (ITS). Among these data, the road surface type classification is one of the most important and can be used in the entire ITS domain. For its widespread application, it is necessary to employ a robust technology for the generation of raw data and to develop of a reliable and stable model to process these data in order to produce the classification. The developed model must operate correctly in different vehicles, under different driving styles and in different environments in which a vehicle can travel. In this work we employ inertial sensors, represented by accelerometers and gyroscopes, which are a safe, non-polluting, and low-cost alternative, ideal for large-scale use. We collect nine datasets with contextual variations, including three different vehicles, with three different drivers, in three different environments, in which there are three different road surface types, in addition to variations in the conservation state and presence of anomalies and obstacles such as potholes and speed bumps. After data collection, these data were used in experiments to evaluate various aspects, such as the influence of the vehicle data collection point, the analysis domain, the model input features, and the data window. Afterwards we evaluated the learning and generalization capacity of the models for unknown contexts. In a third step, the data were used in three Deep Neural Network (DNN) models: LSTM-based, GRU-based, and CNN-based. Through a multi-aspect and multi-contextual analysis, we considered the CNN-based model as the best one, which obtained an average accuracy between the data collection placements of 94.27% for learning and 92.70% for validation, classifying the road surface between asphalt, cobblestone or dirt road segments.

关键词： Roads Sensors Data models Vehicles Sensor systems Deep learning Data collection

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EEG and HRV-Based Assessment of Neurosurgeons Training for Anxiety Regulation and Stress Monitoring

EEG and HRV-Based Assessment of Neurosurgeons Training for A...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Pasquale Arpaia Giovanni Carone Nicolò Castelli Giovanni D'Errico Ludovica Gargiulo Luigi Maffei Giovanna Mastrati Nicola Moccaldi Marco Nalin Alessandro Perin Mauro Picciafuoco Cosimo Puttilli Pedro M. Ramos Rachele Robbio Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy Interdepartmental Center for Research on Management and Innovation in Healthcare (CIRMIS) University of Naples Federico II Naples Italy I.R.C.C.S. Carlo Besta Neurological Institute Milan Italy Department of Applied Science and Technology Polytechnic University of Turin Turin Italy Department of Electrical and Computer Engineering Instituto Superior Técnico University of Lisbon Lisbon Portugal Department of Engineering for Innovation University of Salento Lecce Italy ab medica spa Milan Italy

A neurofeedback (NF) -supported training is proposed to enable neurosurgeons to learn how to regulate their emotions. Electroencephalographic (EEG) signal and heart rate (HR) of 5 neurosurgeons were acquired in 4 sessions while performing NF-based emotion regulation (ER). Subjects tried to counteract anxious and stressful states through NF, that is by decreasing the power in the beta band measured in the midline areas of the scalp. The assessment of the neurofeedback effectiveness was based on the use of Heart Rate Variability (HRV) in combination with EEG. Therefore, EEG signal was exploited in real-time to guide the feedback and also in post processing, together with the HRV, to assess the effectiveness of the whole training. As the EEG signal is concerned, the results showed that the power in the beta band decreased within each session when the ER ability was strengthened, as reported in the literature. On the contrary, the HRV did not exhibit the expected increasing trend within each trial. When considering each the session on the whole, thus computing the HRV on a greater time interval, the HRV during the NF training is typically higher than the HRV computed during the rest and negative baseline phases.

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Microcantilever-integrated photonic circuits for broadband laser beam scanning

arXiv

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arXiv 2022年

作者： Azadeh, Saeed Sharif Mak, Jason C.C. Chen, Hong Luo, Xianshu Chen, Fu-Der Chua, Hongyao Weiss, Frank Alexiev, Christopher Stalmashonak, Andrei Jung, Youngho Straguzzi, John N. Lo, Guo-Qiang Sacher, Wesley D. Poon, Joyce K.S. Max Planck Institute of Microstructure Physics Weinberg 2 Halle06120 Germany University of Toronto Department of Electrical and Computer Engineering 10 King's College Road Ontario TorontoM5S 3G4 Canada Advanced Micro Foundry Pte. Ltd. 11 Science Park Road Singapore Science Park II 117685 Singapore

Laser beam scanning is central to many applications, including displays, microscopy, three-dimensional mapping, and quantum information. Reducing the scanners to microchip form factors has spurred the development of very-large-scale photonic integrated circuits of optical phased arrays and focal plane switched arrays. An outstanding challenge remains to simultaneously achieve a compact footprint, broad wavelength operation, and low power consumption. Here, we introduce a laser beam scanner that meets these requirements. Using microcantilevers embedded with silicon nitride nanophotonic circuitry, we demonstrate broadband, one- and two-dimensional steering of light with wavelengths from 410 nm to 700 nm. The microcantilevers have ultracompact ~0.1 mm2 areas, consume ~31 to 46 mW of power, are simple to control, and emit a single light beam. The microcantilevers are monolithically integrated in an active photonic platform on 200-mm silicon wafers. The microcantilever-integrated photonic circuits miniaturize and simplify light projectors to enable versatile, power-efficient, and broadband laser scanner microchips. © 2022, CC BY-NC-ND.

关键词： Silicon nitride

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