From Circuits to Systems
Earth Precision Agriculture
August 10-14, Calgary, Canada
Agri-Food Sensors & Electronics Conference
Building the hardware foundation for the next generation of smart agriculture with cutting-edge circuits, sensors, and integrated systems
About the Agri-Food Sensors & Electronics Conference Track
The Agri-food Sensors Conference track at EPA 2026 focuses on the sensor-driven foundations of precision agriculture, encompassing physical sensors, electronic interfaces, integrated systems, and the signal processing and intelligence that transform raw measurements into actionable agricultural insight.
This conference track provides a focused forum for researchers and engineers working across hardware, systems, and sensing-centric solutions, designing, building, and deploying the next generation of technologies for the Agri-Food value chain.
We invite high-quality submissions on the design, fabrication, and integration of electronic and sensing hardware, from novel MEMS architectures and wearable livestock monitors to multi-sensor fusion and spatiotemporal AI. Another topic of interest is the development of edge AI and electrochemical sensing with real-world deployment challenges in autonomous and closed-loop agricultural systems. Participants at the agrifood sensors conference will also engage with the broader Earth Precision Agriculture community to ensure that hardware innovations are immediately exposed to domain experts in agronomy, plant, and animal science, accelerating the path from laboratory prototype to field-ready solution.
Sensor & Transducer Design
- Novel Sensor Architectures: Design and fabrication of innovative biosensors, chemical sensors, electrochemical sensors, and physical transducers for agricultural applications.
- MEMS and Nanotechnology: Application of microelectromechanical systems (MEMS) and nanomaterials for creating highly sensitive and selective agricultural sensors.
- Wearable and Implantable Sensors: Development of electronic systems for livestock monitoring, including wearable collars, electronic ear tags, and implantable biosensors.
Circuits & System Integration
- Analog and Mixed-Signal Circuits: Design of low-noise amplifiers, filters, data converters (ADCs/DACs), and signal conditioning circuits for sensor interfaces.
- System-on-Chip (SoC) and System-in-Package (SiP): Integration of complete sensing systems, including microcontrollers, power management, and communication interfaces, into compact, low-cost packages.
- Power Management and Energy Harvesting: Development of ultra-low-power circuits, battery management systems, and energy harvesting solutions (e.g., solar, thermal, vibration, microbial fuel cells) for autonomous sensor nodes.
Communication & Identification Systems
- RFID and NFC Systems: Design of RFID/NFC tags, reader front-ends, and anti-collision protocols for livestock management and supply chain traceability.
- Low-Power Wireless Hardware: Circuit and antenna design for low-power, long-range wireless communication protocols such as LoRaWAN, NB-IoT, and other LPWAN technologies.
Physical, Electrochemical, and Optical Sensing
- In-Situ Plant, Soil, and Water Sensing: Development of electronic probes and systems for real-time measurement of soil nutrients (NPK), pH, moisture, and water quality parameters.
- Gas and Volatile Compound Sensing: Electronic noses and sensor arrays for detecting plant stress indicators (ethylene), spoilage markers, and environmental gases (ammonia, CO2).
- Spectroscopy and Optical Sensing: Design of compact, field-deployable systems based on NIR, fluorescence, or microwave spectroscopy for non-destructive analysis of crop and food quality.
AI at the Sensor and Edge
- On‑Sensor and Near‑Sensor AI: Architectures and circuits that perform inference within or adjacent to the sensing element (e.g., event-based vision, in‑pixel processing, neuromorphic front‑ends for cameras, soil or gas sensors).
- Edge AI Accelerators: Design and deployment of low-power AI accelerators (MCUs with ML, NPUs, FPGAs, neuromorphic chips) integrated into field nodes, UAVs, robots, and livestock wearables.
- TinyML for AgriFood: Ultra‑lightweight models and compression techniques (quantization, pruning, distillation) for always‑on sensing, anomaly detection, and local decision support in constrained devices.
AI‑Driven Signal Processing and Feature Extraction
- Intelligent Signal Conditioning: AI/ML methods that replace or augment traditional filtering, denoising, and calibration for noisy agricultural signals (soil probes, electrochemical sensors, gas sensors, multispectral imagers).
- Self‑Calibration and Drift Compensation: Learning-based calibration, sensor self-diagnosis, and drift correction for long‑term deployments in harsh environments.
- Learning from Weak, Sparse, or Noisy Data: Algorithms for robust inference under missing data, low sampling rates, or low-cost/low-precision sensors typical of field conditions.
Multi‑Sensor Fusion and Spatiotemporal AI
- Sensor Fusion Architectures: AI models that combine data from heterogeneous sensors (e.g., soil–plant–atmosphere measurements, proximal/remote sensing, animal-mounted sensors, machinery telemetry) for improved prediction and state estimation.
- Spatiotemporal Modeling: Deep learning and probabilistic models that capture spatial and temporal dynamics in fields, greenhouses, supply chains, and livestock systems (e.g., yield mapping, stress detection, disease progression, microclimate forecasting).
- Digital Twins and State Estimators: AI-enabled digital twins of crops, animals, and production systems that integrate sensor streams with mechanistic models for real-time monitoring and control.
AI for Autonomous and Closed-Loop AgriFood Systems
- Perception for Robotics and Automation: On‑board AI for perception, navigation, and manipulation in field robots, UAVs, and automated harvesting, weeding, or monitoring platforms.
- Closed‑Loop Control and Decision Support: Learning-based controllers and reinforcement learning for irrigation, fertigation, climate control, feeding, and handling systems using real-time sensor feedback.
- Human‑in‑the‑Loop AI: Interfaces, explainable models, and decision-support tools that translate sensor and AI outputs into actionable insights for farmers, agronomists, and supply-chain stakeholders.
AI System Design, Reliability, and Ethics in AgriFood
- Co‑Design of Hardware, Algorithms, and Protocols: Joint optimization of sensors, embedded platforms, communication protocols, and AI models for energy efficiency, latency, robustness, and cost.
- Robustness, Security, and Trustworthiness: Adversarial robustness, fault tolerance, fail‑safe mechanisms, and secure deployment of AI-enabled sensor networks in critical AgriFood infrastructure.
- Data Governance and Ethics: Methods and frameworks for responsible use of AI in AgriFood sensor systems, including privacy-aware designs, data-sharing models, and equity considerations across regions and farm sizes.
Prof. Danilo Demarchi
Professor
Department of Electronics and Telecommunications
Politecnico di Torino
Dr. Zahra Abbasi
Director
Calgary Sensor Lab
Department of Electrical and Software Engineering
University of Calgary
Dr. Shawana Tabassum
Director
Center for Smart Agriculture Technology
Department of Electrical Engineering
The University of Texas at Tyler
Dr. Qingshan Wei
Associate Professor
Department of Chemical and Biomolecular Engineering
North Carolina State University
Dr. Farhad Maleki
Director
Centre for Precision Agriculture
University of Calgary
Special Issue dedicated to the submission from this track
Selected submissions from the conference will be considered for inclusion in a special issue of The IEEE Transactions on AgriFood Electronics (TAFE) dedicated to showcasing outstanding presentations from the event. This opportunity highlights high-quality research contributions and offers authors a pathway to share their work with the broader agri-food sensors and electronics community.
Authors invited to submit to the special issue have the option to either:
- publish only an abstract or an extended abstract in the EPA proceedings and submit the full paper to the special issue, or
- augment their work by including at least 40% new content, including methodology, results, findings, and discussion points, if they choose to have their short or full papers be included in the EPA proceedings.
Agri-food Sensors Conference at a Glance
- Event Focus: AgriFood Sensors Conference at EPA 2026
- Schedule: August 10-14, 2026
- Location: BMO Centre, Calgary, Alberta, Canada
- Partner Airlines: Air Canada, WestJet, and Lufthansa Group (exclusive discounts available)
- Partner Hotels: Hyatt Regency Calgary and Sandman Signature Downtown, both within walking distance from the conference venue