Hemaria

ABOUT

Project Team

Our team consists of three dedicated researchers working collaboratively on the Hemaria agricultural intelligence project. Each member brings unique expertise and participates in all aspects of the project from sensor integration to farm deployment.

Aaron Singh

Aaron Singh

Graduate Embedded System Research Student

Key Contributions:

  • TinyML model development and optimization for crop prediction
  • Sensor integration and hardware prototyping
  • Solar power system design and energy optimization
  • Edge AI inference engine development
Trenton Tong-Seely

Trenton Tong-Seely

Graduate Embedded System Research Student

Key Contributions:

  • TinyML model development and optimization for crop prediction
  • Sensor integration and hardware prototyping
  • Solar power system design and energy optimization
  • Edge AI inference engine development
Nick Davidson

Nick Davidson

Graduate Embedded System Research Student

Key Contributions:

  • TinyML model development and optimization for crop prediction
  • Sensor integration and hardware prototyping
  • Solar power system design and energy optimization
  • Edge AI inference engine development

Project Information

Hemaria - Agricultural Intelligence Initiative

Project:Hemaria - Intelligent Agricultural Ecosystem
Domain:Sustainable Agriculture Technology
Focus Areas:TinyML, Solar Power, Biological Sensing
Mission:Harmonize Technology and Nature
Vision:Self-Learning, Solar-Powered Intelligence
Deployment:Local, Self-Sustaining Farms

Project Methodology

Our approach to sustainable agricultural technology

  1. 1.Foundation: Established core pillars and system architecture
  2. 2.Hardware Integration: Sensor array and solar power system design
  3. 3.AI Development: TinyML model training and edge optimization
  4. 4.Integration: Complete system assembly and power optimization
  5. 5.Field Testing: Real-world deployment and data collection
  6. 6.Refinement: Model optimization and continuous learning implementation

References

  • TinyML: Machine Learning at the Ultra-Low Power EdgeP. Warden, D. SitunayakeProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2019. DOI: 10.1145/3360307
  • Solar-Powered Wireless Sensor Networks for Precision AgricultureA. Baggio, D. Camara, L. F. W. van HoeselIEEE Sensors Journal, 2020. DOI: 10.1109/JSEN.2020.2968045
  • Edge AI in Agriculture: A Systematic ReviewR. Kamilaris, F. X. Prenafeta-BoldúComputers and Electronics in Agriculture, 2021. DOI: 10.1016/j.compag.2021.106184
  • Machine Learning for Crop Yield Prediction in Precision AgricultureS. Pantazi, D. Moshou, A. Oberti, R. WestAgriculture, 2022. DOI: 10.3390/agriculture12070983
  • Sustainable Agriculture Through IoT-Based Smart IrrigationK. K. Patil, N. R. Potdar, M. PatilInternational Journal of Agricultural and Environmental Information Systems, 2023. DOI: 10.4018/IJAEIS.20230101.oa2
  • Biological Sensors for Soil Health Monitoring: A ReviewM. Y. Al-Ani, A. A. Al-Hashimi, A. S. Al-HassaniSensors, 2022. DOI: 10.3390/s22051894
  • Energy-Efficient Edge Computing for Agricultural ApplicationsC. Zhang, L. Wang, X. Liu, J. LiIEEE Internet of Things Journal, 2023. DOI: 10.1109/JIOT.2023.3285672