Publications

2026

1. 

   Improving watermelon disease classification with stable diffusion-generated images and EfficientNetV2-L-based architecture

   Nitin Rai, Nathan S Boyd, and 2 more authors

   MDPI - AgriEngineering, 2026

   Website

2025

1. 

   Advancing site-specific disease and pest management in precision agriculture: From reasoning-driven foundation models to adaptive, feedback-based learning

   Nitin Rai, Nathan S Boyd, and 2 more authors

   arXiv preprint arXiv:2510.24650, 2025

   Website

2. 

   PhytoSynth: Leveraging multi-modal generative models for crop disease data generation with novel benchmarking and prompt engineering approach

   Nitin Rai, Arnold W Schumann, and 1 more author

   Proceedings of the Computer Vision and Pattern Recognition Conference, May 2025

   Website

3. 

   Deep learning for sunflower (Helianthus annuus L.) mapping and stand counting: Trade-offs between closed vs. open-access methods

   Maria Villamil-Mahecha, Harsh Pathak, and 3 more authors

   Smart Agricultural Technology, Deecember 2025

   Website
4. PMS

   Remote sensing for precision weed management

   Yanbo Huang, Xin Sun, and 4 more authors

   Pest Management Science, Apr 2025

   Website

2024

1. SAT

   A case study on canola (Brassica napus L.) potential yield prediction using remote sensing imagery and advanced data analytics

   Nitin Rai, Harsh Pathak, and 5 more authors

   Smart Agricultural Technology, Dec 2024

   Website
2. JAFR

   Advances in weed identification using hyperspectral imaging: A comprehensive review of platform sensors and deep learning techniques

   Bright Mensah, Nitin Rai, and 2 more authors

   Journal of Agriculture and Food Research, Sep 2024

   Website
3. COMPAG

   Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review

   Arjun Upadhyay, Yu Zhang, and 5 more authors

   Computers and Electronics in Agriculture, Aug 2024

   Website

4. 

   WeedVision: A single-stage deep learning architecture to perform weed detection and segmentation using drone-acquired images

   Nitin Rai, and Xin Sun

   Computers and Electronics in Agriculture, Mar 2024

   Website

5. 

   Agricultural weed identification in images and videos by integrating optimized deep learning architecture on an edge computing technology

   Nitin Rai, Yu Zhang, and 4 more authors

   Computers and Electronics in Agriculture, Jan 2024

   Supp Website

2023

1. Ph.D. Thesis

   Weed identification on drone-captured images using edge device for spot spraying application

   Nitin Rai

   North Dakota State University, Dec 2023

   Website

2. 

   Multi-format open-source weed image dataset for real-time weed identification in precision agriculture

   Nitin Rai, M.V. Mahecha, and 6 more authors

   Data in Brief, Oct 2023

   Bib PDF Supp Website

   @article{rai2023multi,
     title = {Multi-format open-source weed image dataset for real-time weed identification in precision agriculture},
     author = {Rai, Nitin and Mahecha, M.V. and Christensen, Annika and Quanbeck, Jamison and Zhang, Yu and Howatt, Kirk and Ostlie, Michael and Sun, Xin},
     journal = {Data in Brief},
     volume = {51},
     pages = {109691},
     month = oct,
     year = {2023},
     doi = {https://doi.org/10.1016/j.dib.2023.109691},
     publisher = {Elsevier},
   }

3. In: ASABE

   Harvesting the benefits of artificial intelligence in agriculture: An ethical approach

   Nitin Rai

   Int’l Competition: American Society of Agricultural and Biological Engineers, Jul 2023

   PDF
4. J. ASABE

   Aerial-based weed detection using low-cost and lightweight deep learning models on an edge platform

   Nitin Rai, Xin Sun, and 3 more authors

   Journal of the ASABE, Jun 2023

   Website
5. COMPAG

   Applications of deep learning in precision weed management: A review

   Nitin Rai, Yu Zhang, and 5 more authors

   Computers and Electronics in Agriculture, Feb 2023

   Bib PDF Website

   @article{rai2023applications,
     title = {Applications of deep learning in precision weed management: A review},
     author = {Rai, Nitin and Zhang, Yu and Ram, B.G. and Schumacher, Leon and Yellavajjala, R.K and Bajwa, Sreekala and Sun, Xin},
     journal = {Computers and Electronics in Agriculture},
     volume = {206},
     pages = {107698},
     month = feb,
     year = {2023},
     doi = {https://doi.org/10.1016/j.compag.2023.107698},
     url = {https://www.sciencedirect.com/science/article/pii/S0168169923000868},
     publisher = {Elsevier}
   }

2022

1. In: ISPA

   SpotWeeds: A multiclass UASs acquired weed image dataset to facilitate site-specific aerial spraying application using deep learning

   N Rai, Y Zhang, and 3 more authors

   15th International Conference on Precision Agriculture (ICPA), Jul 2022

   PDF Website

2021

1. In: ASABE

   Leveraging transfer learning in ArcGIS Pro to detect “doubles” in a sunflower field

   N Rai, and P Flores

   ASABE Annual International Virtual Meeting (Virtual), Aug 2021

   Bib PDF Supp Website

   @article{rai2021leveraging,
     title = {Leveraging transfer learning in ArcGIS Pro to detect ``doubles'' in a sunflower field},
     author = {Rai, N and Flores, P},
     journal = {ASABE Annual International Virtual Meeting (Virtual)},
     pages = {1},
     month = aug,
     year = {2021},
     organization = {American Society of Agricultural and Biological Engineers},
     url = {https://elibrary.asabe.org/abstract.asp?aid=52515&t=2&redir=&redirType=#:~:text=Results%20showed%20that%20Faster%20R,0.91%2C%20and%20recall%20of%200.96.},
     doi = {10.13031/aim.202100742},
     publisher = {American Society of Agricultural and Biological Engineers}
   }