Prof. Dr. Raziyeh Pourdarbani is a distinguished professor in the Department of Biosystems Engineering at the University of Mohaghegh Ardabili, highly regarded for her academic and research contributions in smart and sustainable agriculture. She holds a Ph.D. in Agricultural Mechanization Engineering from the University of Tabriz and has developed deep expertise in precision agriculture, image processing, artificial intelligence, and machine vision with a focus on non-destructive quality evaluation of agricultural products. Her work advances the use of hyperspectral imaging, convolutional neural networks, metaheuristic algorithms, and Vis-NIR spectroscopy to address key challenges such as fruit bruise detection, nitrogen stress monitoring in plant leaves, and estimation of internal chemical properties in horticultural crops. She has also contributed impactful studies on sustainable energy systems related to agriculture, including biomethane production, hybrid geothermal–solar power plant optimization, and exergy-based diesel engine performance enhancement. Her research portfolio consists of 45 scientific documents with 762 citations from 639 citing documents, supported by an h-index of 17, demonstrating strong global visibility and scholarly influence. Through her innovative work integrating computational intelligence with biosystems engineering, she plays a leading role in advancing intelligent agriculture technologies that enhance productivity, reduce environmental impacts, and support long-term sustainability in the agricultural sector.
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Pourdarbani, R., Ghassemzadeh, H. R., Seyedarabi, H., Nahandi, F. Z., & others. (2015). Study on an automatic sorting system for Date fruits. Journal of the Saudi Society of Agricultural Sciences, 14(1), 83-90.
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Alibaba, M., Pourdarbani, R., Manesh, M. H. K., Ochoa, G. V., & Forero, J. D. (2020). Thermodynamic, exergo-economic and exergo-environmental analysis of hybrid geothermal-solar power plant based on ORC cycle using emergy concept. Heliyon, 6(4).
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Pourdarbani, R., Sabzi, S., Kalantari, D., Karimzadeh, R., Ilbeygi, E., & Arribas, J. I. (2020). Automatic non-destructive video estimation of maturation levels in Fuji apple (Malus pumila) fruit in orchard based on colour (Vis) and spectral (NIR) data. Biosystems Engineering, 195, 136-151.
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Pourdarbani, R., Sabzi, S., Kalantari, D., & Arribas, J. I. (2020). Non-destructive visible and short-wave near-infrared spectroscopic data estimation of various physicochemical properties of Fuji apple (Malus pumila) fruits at different stages. Chemometrics and Intelligent Laboratory Systems, 206, 104147.
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Razieh Pourdarbani, D. K. J. M. M. M., Sabzi, S., Hernández-Hernández, M., & José Luis … (2019). Comparison of different classifiers and the majority voting rule for the detection of plum fruits in garden conditions. Remote Sensing, 11(2546).
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Salimi, M., Pourdarbani, R., & Nouri, B. A. (2020). Factors affecting the adoption of agricultural automation using Davis’s acceptance model (case study: Ardabil). Acta Technologica Agriculturae, 23(1), 30-39.