[MDFSA-2026] Multimodal Data Analysis and Fusion for Smart Agriculture Call for Papers

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22nd International Conference on Content-based Multimedia Indexing, CBMI 2026
 Toulouse, France, October 21-23, 2026

https://cbmi2026.sciencesconf.org/

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[CBMI2026] Special Session - 
Special Session - [ExFMA-2026] Explainability and Fairness in Multimedia Analysis

Smart agriculture relies on Artificial Intelligence (AI) models and
Earth Observation (EO) data. EO data is Big Data, considering only the
peta-bytes per day from the Copernicus EO programme of EU. In this
context, the AI models should learn from/deal with multimodal
data. The development of robust AI models for smart agriculture
fundamentally depends on access to large-scale, high-quality EO
datasets that capture the spatial, temporal and spectral variability
inherent in agricultural systems. Using Deep Learning architectures
for crop classification, phenological monitoring and stress detection
requires massive volumes of data spanning diverse geographical
regions, climatic conditions and growing seasons to ensure model
generalizability.

Multimodal data fusion for agriculture represents an emerging
interdisciplinary field that combines heterogeneous data
sources—including satellite imagery, drone-based sensors, IoT
devices, weather data, soil sensors, and genomic information—to
create comprehensive analytical frameworks for precision agriculture
and sustainable food production. This approach leverages advanced
machine learning, computer vision and signal processing techniques to
integrate temporal, spatial and spectral data streams, enabling more
accurate crop monitoring, yield prediction, disease detection and
resource optimization than any single data modality could achieve
alone.

The field addresses critical challenges in feeding a growing global
population while minimizing environmental impact, drawing on expertise
from remote sensing, agricultural science, data science and
environmental engineering.

This special session welcomes contributions that uses different types
of data for agricultural applications and are related to one or more
topics of interest:
 

Fusion Methodologies and Algorithms

    Deep learning architectures for multimodal integration
    Spatiotemporal data fusion techniques
    Uncertainty quantification in fused predictions
    Transfer learning across different sensors and geographic regions
    Attention mechanisms for modality weighting
    Multimodal large models for agriculture

Data Acquisition and Sensing Technologies

    Hyperspectral and multispectral imaging systems
    UAV-based agricultural monitoring
    IoT sensor networks for soil moisture, nutrients, and microclimate
    Synthetic aperture radar (SAR) for all-weather crop monitoring
    Phenotyping platforms and high-throughput field sensing

Agricultural Applications

    Crop yield forecasting and early warning systems
    Plant disease and pest detection
    Precision irrigation and fertilization management
    Soil health assessment and carbon sequestration monitoring
    Livestock monitoring through integrated sensor systems

Emerging Challenges

    Edge computing and on-farm processing
    Data standardization and interoperability across platforms
    Climate adaptation and resilience modeling

Integration with Decision Support

    Explainable AI for farmer-facing applications
    Real-time alert systems and recommendation engines
    Economic modeling and cost-benefit analysis
    Policy implications and regulatory framework

Important dates :

Paper deadline: 20 APRIL  2026 

Notification: 22 MAY  2026

Camera-ready: 15 JUNE 2026

Paper submission  : Author Guidelines

Please indicate in the comments that this paper is for SS  MDFSA-2026

SS chairs

    Prof. Mihai Ivanovici, Transilvania University of Brasov, Romania
    Prof. Corneliu Florea, National University of Science and Technology POLITEHNCA of Bucharest, Romania