ResearchGate record · 2026
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Article · Big Data In Agriculture · August 2026
Co-authored with A. K. Roy and D. Wasti. Featured research on varietal comparison in Morang District, Nepal.
Article · Tropical Agrobiodiversity · August 2026
Co-authored with Sabina Upadhyaya, Sachin Chapagain, and Aayushma Chemjong.
Article · Tropical Agrobiodiversity · July 2026
Co-authored with Aayushma Chemjong, Sabina Upadhyaya, and Indona Rai.
NAXA Nepal · February 2025
Training in CROPGRO and CERES crop modules, genetic coefficient calibration for local varieties, simulation of climate–crop interactions and environmental stress responses, model validation against field experimental data, and integration of DSSAT outputs with the R statistical pipeline (tidyverse, agricolae) for ANOVA and variance analysis. Applied in crop physiology research and climate scenario analysis for Nepal’s subtropical terai conditions.
View CertificateDataCamp
Comprehensive certification covering Python programming, data manipulation, visualization, and machine learning fundamentals.
Professional certificate program covering data analysis process, tools, and techniques including SQL, R, and Tableau.
DataCamp
Foundational course covering data science methodology, statistical concepts, and practical applications.
MinnaLearn
Introduction to artificial intelligence concepts, machine learning principles, and AI applications in various domains.
Insights, tutorials, and thought leadership on precision agriculture, remote sensing, AI technology, and data science from my professional journey
I'm a student in Nepal with no MS, no research grant, and no institutional backing. Here's exactly how I'm positioning myself anyway — through Google Earth Engine, field experiments, applied ML, and public documentation. A specific problem is more credible than a general skillset.
Read on LinkedInA year ago, I was counting tillers in a rice field. Today, I have 8 manuscripts under review, analyze farmland from space using Sentinel-2 imagery, and build ML models to predict crop yield. The path forward is built one uncomfortable step at a time.
Read on LinkedInI learned GIS. Then remote sensing. Then machine learning. Each step felt like starting over. But now I see them as one system — and the moment I stopped learning them separately, everything accelerated.
Read on LinkedInGIS gave me spatial thinking. Remote sensing gave me field-scale visibility. ML gave me predictive power. For agriculture, the real power is in combining all three — not treating them as separate skills.
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