Melba M. Crawford
Engineering · Purdue University West Lafayette
Publications
246
Citations
7,733
Est. group size
~3
Recurring co-author estimate
Active years
57
Publishing since 1970
Melba M. Crawford's research focuses on analyzing remote sensing data—images and sensor measurements collected from satellites, aircraft, and drones—using machine learning and deep learning methods. A major application area is agriculture, where her work predicts crop traits like yield, biomass, and nitrogen use from drone-based imagery, LiDAR (laser-based 3D scanning), and hyperspectral cameras (which capture many more color bands than standard cameras). She also develops general-purpose AI models (foundation models and transformers) for classifying and interpreting multispectral and hyperspectral imagery across different sensor types.
Publication output has fluctuated over the past decade, with a peak around 2021 and generally lower yearly counts since 2022, suggesting a slowing pace in recent years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Advancing Efficient Vision Foundation Models for Analysis of Multispectral Imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2026
- A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation via Synergistic Pseudo-Labeling and Generative Learning
2025
- Michael R. Inggs (1951–2025) [In Memoriam]
IEEE Geoscience and Remote Sensing Magazine · 2025
- A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation viaSynergistic Pseudo-Labeling and Generative Learning
arXiv (Cornell University) · 2025
- Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments
Frontiers in Plant Science · 2024
- Investigation of Hierarchical Spectral Vision Transformer Architecture for Classification of Hyperspectral Imagery
IEEE Transactions on Geoscience and Remote Sensing · 2024
- Attention Guided Semisupervised Generative Transfer Learning for Hyperspectral Image Analysis
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2024
- Investigation of Hierarchical Spectral Vision Transformer Architecture for Classification of Hyperspectral Imagery
arXiv (Cornell University) · 2024
- A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers
Frontiers in Plant Science · 2023
- SMAP soil moisture data assimilation impacts on water quality and crop yield predictions in watershed modeling
Journal of Hydrology · 2023
- CNN-Mixer Hierarchical Spectral Transformer for Hyperspectral Image Classification
2023
- Deep Learning Models Using Multi-Modal Remote Sensing for Prediction of Maize Yield in Plant Breeding Experiments
2023
- Foreword
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2023
- Estimation of Leaf Area Index in Maize from Uav-Based Lidar Point Cloud Data Via Pointnet++
SSRN Electronic Journal · 2023
- Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling
Frontiers in Plant Science · 2023
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing×8
- Remote Sensing×7
- arXiv (Cornell University)×6
- IEEE Transactions on Geoscience and Remote Sensing×5
- Frontiers in Plant Science×4
- Huapeng Li
Engineering · The Ohio State University
- Susmita Ghosh
Engineering · Purdue University West Lafayette
- Hessah Albanwan
Engineering · The Ohio State University
- Okan K. Ersoy
Engineering · Purdue University West Lafayette
- Kang Ni
Engineering · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile