Assoc. Prof. Dr. Mohsen Edalat | Machine Learning for Computer Vision | Editorial Board Member
Associate Professor | Shiraz University | Iran
Dr. Mohsen Edalat an accomplished researcher from Shiraz University, Iran, has made notable contributions to the fields of machine learning geospatial modeling and smart agriculture. With an impressive research record comprising 39 scientific publications and over 614 citations Dr. Edalat has demonstrated sustained academic productivity and influence in computational and environmental sciences. His research emphasizes the integration of advanced data-driven algorithms with ecological and agricultural systems to enhance sustainability and decision-making processes.Among his recent works Dr. Edalat has explored diverse applications of machine learning for ecological and agricultural optimization. His 2025 publications include studies on predicting nepetalactone accumulation in Nepeta persica through machine learning and geospatial analysis modeling ecological preferences of Kentucky bluegrass under varying water conditions (Water Switzerland) and mapping early-season dominant weeds using UAV-based imagery to support precision farming. These investigations reflect his innovative approach to merging remote sensing artificial intelligence and environmental modeling to address complex agroecological challenges.With an h-index of 11 and collaborations with more than 60 co-authors Dr. Edalat’s work highlights strong interdisciplinary engagement and a commitment to advancing data-driven sustainability. His studies contribute not only to the scientific community but also to practical agricultural applications that promote resource efficiency and ecological resilience. Through his ongoing research Dr. Edalat continues to shape the evolving landscape of smart agriculture and environmental informatics demonstrating the global relevance and societal value of computational intelligence in natural systems.
Featured Publications
1. Edalat, M., et al. (2025). Predicting nepetalactone accumulation in Nepeta persica using machine learning algorithms and geospatial analysis. Scientific Reports.
2. Edalat, M., et al. (2025). Modeling the ecological preferences and adaptive capacities of Kentucky bluegrass based on water availability using various machine learning algorithms. Water (Switzerland).
3. Edalat, M., et al. (2025). Early season dominant weed mapping in maize field using unmanned aerial vehicle (UAV) imagery: Towards developing prescription map. Smart Agricultural Technology.
Dr. Mohsen Edalat’s research integrates machine learning, geospatial analytics, and agricultural science to enhance crop management and environmental sustainability. His innovative work advances precision agriculture, supporting data-driven decisions that improve resource efficiency, boost food security, and promote sustainable development at a global scale.