Dr. Vali Rasooli Sharabiani is a distinguished Professor at the University of Mohaghegh Ardabili, Iran, whose research centers on precision agriculture, smart farming technologies, and non-destructive measurement methods for sustainable crop production and food processing. His scientific work integrates artificial intelligence, hyperspectral imaging, and multivariate data analysis to enhance agricultural efficiency, resource management, and environmental protection. Dr. Sharabiani’s contributions have significantly advanced the understanding of variable rate technology, remote sensing, and the application of machine learning models such as ANNs, ANFIS, and fuzzy logic in agricultural systems. His interdisciplinary approach bridges engineering, agronomy, and data science, making his research highly influential in both academic and industrial sectors. With more than 1,500 citations, an h-index of 21, and an i10-index of 37 on Google Scholar, along with high-impact publications indexed in Scopus, his scholarly achievements reflect strong global recognition. Dr. Sharabiani’s research outputs are widely referenced in the fields of agricultural mechanization, energy-efficient drying systems, and precision monitoring of crop and soil properties.
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Featured Publications
Kaveh, M., Sharabiani, V. R., Chayjan, R. A., & Taghinezhad, E. (2018). ANFIS and ANNs model for prediction of moisture diffusivity and specific energy consumption of potato, garlic, and cantaloupe drying under convective hot air dryer. Information Processing in Agriculture, 5(3), 372–387.
Kaveh, M., Chayjan, R. A., Taghinezhad, E., Sharabiani, V. R., & Motevali, A. (2020). Evaluation of specific energy consumption and GHG emissions for different drying methods (Case study: Pistacia Atlantica). Journal of Cleaner Production, 259, 120963.
Jahanbakhshi, A., Kaveh, M., Taghinezhad, E., & Rasooli Sharabiani, V. (2020). Assessment of kinetics, effective moisture diffusivity, and specific energy consumption in the pistachio kernel drying process in microwave drying. Journal of Food Processing and Preservation, 44(6), e14449.
Jahedi Rad, S., Kaveh, M., Sharabiani, V. R., & Taghinezhad, E. (2018). Fuzzy logic, artificial neural network, and mathematical model for prediction of white mulberry drying kinetics. Heat and Mass Transfer, 54(11), 3361–3374.
Rasooli Sharabiani, V., Kaveh, M., Abdi, R., Szymanek, M., & Tanaś, W. (2021). Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling. Scientific Reports, 11(1), 9155.