Agriculture is the heartbeat of the world, where every seed sown is a step towards a future of abundance, sustainability, and hope.

Research Endowment Fund

The fund supports innovative projects and academic research in Nepal, fostering scientific advancement and community development. Contributions sustain cutting-edge studies, including:


Total fund collected: NRs 5000

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Bikas Basnet

Independent Researcher

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Bikas Basnet

Research Interests

  • Genomics & Plant Breeding
  • Plant-Microbe Interactions
  • Quantitative Genetics
  • Computational Biology & Data Science
  • Crop Science

Mission & Vision

As an enthusiastic graduate student in agriculture, I am curious about crop improvement—encompassing genomics, plant-microbe interactions, quantitative genetics, and modern plant breeding tools—and computational genetics, machine learning, and deep learning. Through rigorous academic training and hands-on internships, I aim to bridge the gap between research stations and farmers’ fields by fostering innovation exchange and capacity building. My mission is to address food and nutrition insecurity in developing countries through impactful, collaborative projects that advance sustainable agriculture and empower farming communities.

Connect & Explore

Ongoing Projects

Elipse plot
PCA-based ellipse plot.
Journey of wheat Cleveland Bayesian prediction
Figure showing the journey of wheat release in Nepal and a Cleveland dot plot for Bayesian interactive prediction.
Training and ROC Plots
Core Metrics
Custom CNN (CropDiseaseCNN): Accuracy vs loss vs time vs epochs and Test core metrics heat map
Confusion matrix
Figure depicting correct and incorrect predictions across classes.

Accepted Publications

Which wheat varieties unveil strong adult plant resistance to common pathotypes of leaf rust (Puccinia triticina) while maintaining prime yield?


Assignments

Publications

2025

2024

2023

2022

2021

For an updated CV, please contact me.

Resources for Students

Innovation

Plant Diseases Diagnosis AI

MobileNetV2 Architecture for Mungbean Disease Classification

Holographic 3D diagram of MobileNetV2 architecture showing convolutional layers, 17 inverted residual blocks, fully connected neurons, and parameters for mungbean disease classification.

Figure: Futuristic holographic 3D visualization of the MobileNetV2 architecture, depicting all convolutional and hidden layers, neuron connections, and parameters in an immersive sci-fi aesthetic (Bikas Basnet © 2025-2026).

Embedded Sunburst Test