Suraj A. Yadav
Mississippi State University, Mississippi state university, Indian Institute of Technology BHU
0000-0002-8158-7207
About
I specialize in computational electromagnetic modeling and physics-informed artificial intelligence to develop scalable decision-support systems for forest applications, vegetation modeling, and precision agriculture.
My research integrates electromagnetic scattering theory, radiative transfer modeling, and AI-driven inversion frameworks to better understand and quantify vegetation structure and biophysical properties. I develop physics-guided deep learning models that combine LiDAR, hyperspectral, multispectral, and GNSS-based sensing data to retrieve plant traits, assess forest structure, and simulate signal-canopy interactions across multiple spatial scales.
My work spans both agricultural and forested ecosystems. In precision agriculture, I designed UAV-based sensing pipelines that fuse computer vision, deep learning, and physical modeling to estimate crop health, nitrogen status, canopy structure, and productivity. In forest applications, I developed radiorealistic vegetation models to simulate electromagnetic wave propagation and scattering, supporting radar, microwave, and GNSS signal transmittance studies for structural assessment and environmental monitoring.
At Mississippi State University, I contribute to AI-driven autonomy and advanced sensing research, focusing on scalable modeling frameworks that bridge computational physics with data science. In my prior role at the High-Performance Computing Collaboratory (HPC²) and the Department of Electrical and Computer Engineering, I worked on electromagnetic solvers and radiative modeling tools to simulate forest canopies for radar and microwave remote sensing applications. Across all projects, my focus remains on building interpretable, physics-consistent AI systems that translate complex Earth observation data into actionable insights for sustainable agriculture, forest management, and environmental resilience.
My technical expertise includes Python (PyTorch, TensorFlow, CUDA), electromagnetic and radiative transfer modeling, LiDAR simulation, hyperspectral analysis, and high-performance computing environments. I am also actively engaged in interdisciplinary collaboration, proposal development, and mentoring graduate researchers.
Employment
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Mississippi State University Post doctoral Research Associate2024 - Present
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Mississippi state university Post doctoral Research Associate2022 - 2024
Education
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Indian Institute of Technology BHU Doctor of Philosophy2017 - 2022
Projects & Funding
Projects & funding information is unavailable.
Publications (21)
- Physics-Guided Differentiable PROSPECT-DNN Model for Inversion of Maize Leaf Biochemistry Save
- Calibrated PATH-Based Modeling and Overlapping Cylindrical Voxelization for Maize LAI Mapping Using UAS-LiDAR Save
- Deep Transfer Learning for UAV-Based Cross-Crop Yield Prediction in Root Crops Save
- Context-Aware Deep Learning Model for Yield Prediction in Potato Using Time-Series UAS Multispectral Data Save
- Improved radar vegetation water content integration for SMAP soil moisture retrieval Save
- Radiometric and Modified RossThick-LiSparse BRDF Correction for Low-Altitude UAV Data at Varying Solar-Sensor Geometries for Time-Series Analysis Save
- Development of a new vegetation modulated soil moisture index for the spatial disaggregation of SMAP soil moisture data product Save
- Evaluating the Transferability of a Bistatic Radiative Transfer Model to Monostatic Scattering for Wheat Crop with Sentinel-1 SAR Dual Polarimetric Data Save
- Fusion of Optical and SAR Data Using Three Approaches for the Estimation of LAI With Modified Integral Equation Model Save
- Incorporation of first-order backscattered power in Water Cloud Model for improving the Leaf Area Index and Soil Moisture retrieval using dual-polarized Sentinel-1 SAR data Save