Ph.D. in Irrigation and Drainage Engineering, Department of Irrigation and Reclamation Engineering, University of Tehran

Agricultural AI researcher with expertise in climate-smart agriculture, decision support systems, and machine learning applications for water and nutrient management. My research combines data-driven modeling, environmental analytics, crop modeling, and software development to improve agricultural productivity, resource-use efficiency, and climate resilience. I have developed an AI-powered web-based decision support system for greenhouse management and published research on machine learning, irrigation optimization, evapotranspiration modeling, soil salinity, and precision water management. My long-term research goal is to develop intelligent digital solutions that support sustainable and climate-resilient agricultural systems.

My research focuses on:

• Agricultural Artificial Intelligence and Machine Learning
• Decision Support Systems for Smart Agriculture
• Climate-Smart Agriculture
• Environmental Data Analytics
• Precision Irrigation and Fertigation
• Crop and Agro-hydrological Modeling
• Water and Nutrient Use Efficiency
• Remote Sensing and Environmental Monitoring

• Developed a web-based Decision Support System (DSS) for irrigation and fertigation scheduling of greenhouse cucumber under variable water quality conditions.
• Integrated machine learning models for environmental and agricultural predictions (including evapotranspiration and radiation-related components).
• Designed system modules for ETo and ETc estimation, crop irrigation requirement, fertilization optimization, solar radiation estimation, climate feasibility analysis (greenhouse energy demand analysis).
• Implemented data-driven decision-making workflows using real-time and historical climate datasets.
• Built backend system using Django and Python, enabling scalable integration of ML models with agricultural databases.
• Applied optimization and statistical learning methods to support sustainable water and nutrient management strategies.
• Technologies: Python, Django, HTML/CSS, Scikit-learn, Pandas, Numpy, Optuna

• Developed and integrated framework combining the SWAP agro-hydrological model with multi-source satellite data (Sentinel-2, Landsat-8) to estimate daily root-zone soil moisture at high spatial resolution for precision irrigation management.
• Applied inverse modeling and data assimilation techniques using genetic algorithms to optimize soil hydraulic parameters and incorporate vegetation indices, reducing dependence on in-situ measurements.
• Validated the approaches across multiple agricultural fields (wheat and maize) under diverse climatic and soil conditions in Iran.
• Demonstrated applicability for variable-rate irrigation strategies to improve water use efficiency in water-scarce agricultural regions.
• Utilized advanced remote sensing methods including OPTRAM and optical satellite image processing for soil moisture and vegetation monitoring.
• Technologies: SWAP, MATLAB, Remote Sensing (Sentinel-2, Landsat-8), Genetic Algorithms.

1. Noory, H., Khoshsima, M., Tsunekawa, A., Tsubo, M., Haregeweyn, N., & Pashapour, S. (2025). Developing a method for root-zone soil moisture monitoring at the field scale using remote sensing and simulation modeling. Agricultural Water Management, 308, 109263.
2. Khoshsimaie Chenar, M., Noory, H., Soltani Salehabadi, F., & Motesharezade, B. (2026). Salinity tolerance threshold in greenhouse cucumber cultivation: a comparative analysis of mathematical models. Irrigation Science, 44(2), 35.
3. Hoseini, S. M., & Khoshsimaie Chenar, M. (2025). Application of machine learning algorithms in groundwater level prediction in the Ardabil aquifer. Iranian Journal of Soil and Water Research, 56(4), 1041-1057.
4. Khoshsimaie Chenar, M. , Noory, H. , Liaghat, A., Soltani Salehabadi, F. and Motesharezadeh, B. (2025). Evaluation of the accuracy of different machine learning algorithms in predicting greenhouse cucumber crop evapotranspiration. Water and Irrigation Management, 15(3), 563-583.
5. Khoshsimaie Chenar, M., Tafteh, A., & Ebrahimipak, N. (2025). Modeling Greenhouse Cucumber Evapotranspiration Using Machine Learning: A Random Forest Approach Versus Traditional and Non-linear Crop Coefficients. Water and Soil Management and Modelling, 5(2), 69-87.
6. Khoshsimaie Chenar, M., Liaghat, A., Noory, H., Soltani Salehabadi, F., & Motesharezadeh, B. (2025). Impact of Different Salinity Levels and Irrigation Water Amounts on Yield and Water Productivity of Greenhouse Cucumber in Two Autumn-Winter and Spring-Summer Growing Periods. Water Management in Agriculture.
7. Golshani, H., & Khoshsimaie Chenar, M. (2024). Evaluation of AquaCrop and SWAP models in simulating the growth and biomass of different maize cultivars under the conditions of using saline water with drip irrigation system. Iranian Journal of Soil and Water Research, 55(4), 615-636.
8. Khoshsimaie Chenar, M., Noory, H., & Mahmoudi Molamahmoud, Z. (2021). Evaluation of SWAP model in estimating soil water content, salinity and yield of three forage maize cultivars under saline water use conditions. Water and Irrigation Management, 11(3), 495-512.

Email: khoshsima.mortaza@ut.ac.ir