Rajkumar Dhakar
New Delhi, Delhi| Grant Category: | Fulbright-Nehru Postdoctoral Research Fellowships |
| Project Title: | Localized Weather and Aerial Imagery-based AI-driven Fungal Diseases Prediction Models for Precision Apple Crop Protection |
| Field of Study: | Agricultural Sciences |
| Home Institution: | ICAR-Indian Agricultural Research Institute, New Delhi, Delhi |
| Host Institution: | Washington State University , Pullman, WA |
| Grant Start Month: | August 2023 |
| Duration of Grant: | Twelve months |
Brief Bio:
Dr. Rajkumar Dhakar obtained his B.Sc. in agriculture from the College of Agriculture Dhule, Mahatma Phule Krishi Vidyapeeth Rahuri, Maharashtra in 2008. He obtained his master’s and Ph.D. degrees in 2010 and 2020, respectively, from the Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi. Currently, he is a faculty member at IARI. In his doctoral research, Dr. Dhakar focused on developing a novel spatial field scale wheat yield forecasting system through integration of satellite remote sensing derived leaf area index into agroecosystem model along with weather forecast. He has published about 30 research papers in journals of national and international repute. Currently, he is working on projects such as mapping tillage patterns in rice-wheat cropping systems using satellite data, crop yield forecasting, and precision nitrogen management in wheat using smartphone and satellite data.
Dr. Dhakar is a recipient of the ICAR JRF Fellowship (2008), IARI Merit Medal for M.Sc. (2011), CSIR-UGC JRF Fellowship (2010), DST-INSPIRE fellowship (2011), Best M.Sc. thesis award from the Association of Agrometeorologist (2011), IARI Merit Medal for Ph.D. (2021), Best PhD thesis award from the Association of Agrometeorologist (2021), Best Oral Presentation awards from ISRS (2016) and from the Association of Agrometeorologist (2014).
During his Fulbright-Nehru Postdoctoral Research fellowship, Dr. Dhakar is assessing general epidemiological models of airborne fungal infections for the prediction of diseases in apples; mapping and detecting diseases through small unmanned aerial system using artificial intelligence techniques; and evaluating economic feasibility of precision fungicide applicators over conventional ones.