Skip to Main Content

Meet Visiting Scholar Rajiv Ranjan

I am a PhD scholar at Plaksha University (Mohali, India), specializing in Computer Vision and Geospatial Intelligence, with a focus on AI and machine learning applications in remote sensing, satellite/UAV-based analytics, and precision agriculture.

Rajiv in front of a white screen As a visiting scholar in the Department of Agricultural & Biological Engineering (ABE) at Purdue University, I am working under the mentorship of Dr. Dharmendra Saraswat a leading expert in environmental and digital agriculture and geospatial AI.

My research broadly explores the integration of AI‑driven methodologies for crop yield forecasting, crop‑type mapping, stubble burning detection, and environmental monitoring—leveraging multispectral, satellite, and UAV‑derived datasets. I have hands‑on experience with machine learning frameworks including ensemble regressions, CNNs, RNNs, LSTMs, transformers, and generative models. I also work extensively with image preprocessing workflows such as radiometric and geometric corrections, Top‑of‑Atmosphere (TOA) conversions, spectral index analysis, and classification techniques.

I am proficient in geospatial platforms and tools such as Google Earth Engine, QGIS, SNAP, and PIX4D, and I code in Python using frameworks like TensorFlow, PyTorch, RasterIO.

I have presented and published my work at international venues including NeurIPS, IGARSS, ICPR, IEEE GRSL, and ACM COMPASS. Through the fellowship at Purdue, I look forward to collaborating with experts in digital agriculture and geospatial AI, and contributing to scalable, AI‑powered solutions for sustainable, climate‑smart farming systems.

As part of my doctoral work, I am advancing Self‑Supervised Learning (SSL)‑based time‑series Geospatial Foundation Models (GFMs) for sugarcane yield and pol prediction. During my time at Purdue, I will focus on developing and evaluating multi‑scale, multi‑source data fusion techniques, combining satellite and UAV data for sugarcane growth monitoring. I plan to pretrain a Geospatial Foundation Model using paradigms such as SSL, multi‑objective learning, and multimodal fusion, then fine‑tune it on labeled datasets for pre‑harvest yield and pol prediction.

My goals include rigorous model benchmarking, scalability assessment, and performance validation across diverse agro‑climatic conditions—striving to build robust, region‑adaptive geospatial AI systems.

I am thrilled to be working under Dr. Saraswat’s mentorship as a visiting scholar at Purdue University, advancing impactful research in precision agriculture and geospatial intelligence.

Featured Stories

Ag Barometer
Farmer sentiment weakens as producer confidence in future wanes

Farmer sentiment dipped for the third straight month in August, with the Purdue University-CME...

Read More
John Deere Auto Unload
Harvesting in sync: Purdue and John Deere develop automated unloading technology

Harvest is one of the most demanding times in a farmer’s year. The clock is always ticking,...

Read More
Jinyuan Shao
Jinyuan Shao - Graduate Ag Research Spotlight

From a young age, Jinyuan Shao was drawn to computers and fascinated by how things worked. In...

Read More
Purdue College of Agriculture.
Virtual event offers chance to explore opportunities in graduate studies

The Zoom event will be 9 a.m.-noon (EDT) Wednesday, September 10, and will explore graduate study...

Read More
Girl riding a horse
Finding her path: How Purdue helped a transfer student become a mentor and leader

From the time she was in high school in Hebron, Indiana, Gwen Weaver knew she wanted to become a...

Read More
Yellow and brown mosquito larvae, resembling tiny wormlike creatures, floating in clear water puddles on a desk for examination.
Expert tips for minimizing threat of West Nile virus, other vector-borne disease

As mosquitoes and people across the country test positive for West Nile virus (WNV), Purdue...

Read More
To Top