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Sahameh Shafiee

Sahameh Shafiee

Forsker

  • Genetikk, evolusjon og bærekraftig planteproduksjon

About Me
I am a Section Leader and Agricultural Technology Scientist at NMBU, holding a PhD in Biosystems Engineering with a focus on the mechanics of agricultural machinery. I lead the Section of Genetics, Evolution, and Sustainable Plant Production, where we study how plant traits are influenced by genes, pests, other plants, and environmental interactions. My work bridges plant science and engineering to advance digital, data-driven, and technology-enabled agriculture.

Research Vision
I am passionate about transforming agriculture through sustainable, resilient, and productive crop systems that leverage technology and data. My research focuses on plant phenotyping, precision agriculture, and AI-driven crop production, integrating plant breeding, physiology, agronomy, and engineering to generate actionable insights for farmers, breeders, and research teams. My goal is to create solutions that combine scientific rigor with real-world impact.

Key Projects
I lead several high-impact projects at the intersection of biology and technology:

  • SmartWheat – Harnessing AI Models to Develop Climate-Resilient Wheat Varieties for Sustainable Agriculture
    This project applies advanced technologies to develop drought-tolerant wheat varieties. We use AI, digital twins, UAVs, and phenotyping platforms to enhance wheat performance under climate stress and support sustainable crop production.
  • Smart_IPM – Smart Solutions for Pest Control in Strawberries
    We combine imaging technology and AI to develop precision pest management strategies for strawberry cultivation.
  • Soldeling i Landbruket (Sunsharing in Agriculture) – Studying Agrivoltaics Systems
    This project investigates how agrivoltaics (solar panel + crop systems) affect crop performance in Norway. We analyze light, growth, and yield responses to optimize the integration of renewable energy and agriculture.
  • ADAPT GRAIN – Towards Climate-Smart Cereal Production in Norway
    This project examines the impact of future climate scenarios on cereal crop production. Through modeling and field trials, we develop strategies to ensure resilient, high-yield cereal systems under changing climatic conditions.

Teaching & Supervision
I lead the course High Throughput Field Phenotyping and Vegetation Mapping, providing hands-on training in UAVs, sensors, and data analysis. Since 2018, I have supervised five PhD students and more than sixteen Master’s students on interdisciplinary projects that combine biology and technology. Mentoring and fostering collaborative research environments is central to my work.

Scientific Leadership & Community Building
I organize and speak at international field days and workshops, host research events on remote sensing and phenotyping technologies, and actively contribute to Nordic and global research networks.

Work With Me
I am actively seeking motivated students and collaborators:

  • Students (MSc & PhD): If you are interested in AI in agriculture, precision farming, plant phenotyping, or climate-resilient crop systems, I welcome you to join my research team.
  • Collaborators: I am open to partnerships with research institutions, AgriTech companies, plant breeding and seed companies, and data science/AI groups.

Please feel free to contact me to discuss potential projects or ideas. I enjoy building interdisciplinary teams and exploring innovative solutions for sustainable agriculture

 

  • Publikasjoner

    Forskerprofil med publikasjoner i vitenarkivet

    1. Shafiee, S., Lied, L.M., Burud, I., Dieseth, J.A., Alsheikh, M., Lillemo, M., 2021. Sequential forward selection and support vector regression in comparison to LASSO regression for spring wheat yield prediction based on UAV imagery. Electron. Agric. 
    2. David, E., Serouart, M., Smith, D., Madec, S., Velumani, K., Liu, S., Wang, X., Pinto, F., Shafiee, S., Tahir, I.S.A., Tsujimoto, H., Nasuda, S., Zheng, B., Kirchgessner, N., Aasen, H., Hund, A., Sadhegi-Tehran, P., Nagasawa, K., Ishikawa, G., Dandrifosse, S., Carlier, A., Dumont, B., Mercatoris, B., Evers, B., Kuroki, K., Wang, H., Ishii, M., Badhon, M.A., Pozniak, C., LeBauer, D.S., Lillemo, M., Poland, J., Chapman, S., de Solan, B., Baret, F., Stavness, I., Guo, W., 2021. Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods. Plant Phenomics 2021, 9846158.
    3. Shafiee, S.; Minaei, S (2018). Combined Data Mining /NIR Spectroscopy for Purity Assessment of Lime Juice. Infrared Physics and Technology, 91, 193-199. 
    4. Minaei, S.; Shafiee, S.; Polder, G.; Moghadam-Charkari, N.; Ruth, S.V.; Barzegar,M.; Zahiri,      ; Alewijn, M.;Kuś. P.M. (2017). VIS/NIR Imaging Application for Honey Floral Origin Determination. Infrared Physics and Technology, 86, 218-225. 
    5. Ghasemi-Varnamkhasti, M.; Goli, R.; Forina, M., Mohtasebi, S.; Shafiee, S.; Naderi-Boldaji, M. (2016). Application of image analysis combined with computational expert approaches for shrimp freshness evaluation. International Journal of Food Properties. 2202-2222.
    6. Shafiee, S.; Minaei, S.; Moghaddam-Charkari, N.; Barzegar, M. (2014). Honey characterization using computer vision system and artificial neural networks. Food Chemistry, 159, 143–50. 
    7. Shafiee, S.; Minaei, S.; Moghaddam-Charkari, N.; Ghasemi-Varnamkhasti, M.; Barzegar, M. (2013). Potential application of machine vision to honey characterization. Trends in Food Science & Technology. 30:174-177.
    8. Kouchakzadeh, A; Shafeei, S. (2010). Modeling of microwave-convective drying of pistachios. Energy Conversion and Management. Vol.51. 2012–2015. 
    9. Shafiee, S.; Polder, G.; Minaei, S.; Moghadam-Charkai, N; Piotr, M.; Ku, M.; Ruth, V. (2016). Detection of Honey Adulteration using Hyperspectral Imaging.5th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture, Seattle, USA.

  • Forskning og prosjekter