Justine Kipruto Kitony | Plant Genomics | Excellence in Research Awards

Dr. Justine Kipruto Kitony | Plant Genomics | Excellence in Research Awards

Dr. Justine Kipruto Kitony | Salk Institute for Biological Studies | United States

Dr. Justine K. Kitony is a postdoctoral fellow in Plant Genomics and Breeding at the Salk Institute for Biological Studies. With over a decade of experience in plant genomics, bioinformatics, and breeding, he integrates cutting-edge sequencing technologies with field phenotyping and genomic prediction to uncover trait-function relationships in key crops. Him work bridges fundamental science and agricultural application, contributing to climate-resilient breeding strategies and sustainable seed systems. Justine has led and co-authored high-impact publications in top journals such as Nature and Nature Communications, with notable contributions in cannabis, baobab, and sorghum pangenomics. Passionate about collaborative science, he has mentored early-career researchers and coordinated cross-functional research teams across Asia, Africa, and the U.S. he is driven by the goal of enhancing crop performance under environmental stress while preserving biodiversity and advancing food and energy security globally.

Publication Profile: 

Google Scholar

Education:

Dr. Kitony holds a Ph.D. in Agricultural Sciences (Quantitative Genetics and Genomics) from Nagoya University, Japan, where he developed a novel nested association mapping (NAM) population in rice to dissect complex traits. he earned him M.Sc. in Bioinformatics from Fujian Agriculture and Forestry University, China, focusing on transcriptome analysis of rice blast resistance. Him academic foundation in computer science and databases was laid with a B.Sc. in Information Technology from RMIT University, Australia. Across these programs, he has acquired multidisciplinary expertise combining computational biology, statistical genetics, molecular biology, and plant breeding. This diverse educational background uniquely positions him to lead genomics-driven research for sustainable crop improvement. Him international academic journey reflects him adaptability and global research outlook, equipping him with the skills necessary to solve real-world agricultural challenges using cutting-edge tools.

Experience:

Currently a Postdoctoral Fellow at the Salk Institute, Dr. Kitony leads the sorghum pangenome project within the Harnessing Plants Initiative. he integrates ONT and HiFi sequencing with trait mapping, GWAS, and CRISPR target discovery for crop improvement. Previously, at Kenya Agricultural and Livestock Research Organization (KALRO), he designed and managed large-scale field trials for rice and cotton, implemented genotyping pipelines, and supported seed system delivery. he also has industry experience as a systems consultant, managing large-scale databases and automating data workflows. Him experience spans from field phenotyping and molecular biology to cloud-based bioinformatics and genomic prediction. A proven leader, he has mentored students, authored key publications, and collaborated across disciplines and geographies. Him field-to-lab translational research expertise makes him an invaluable asset in advancing data-driven, sustainable breeding solutions.

Awards and Honors:

Dr. Kitony’s contributions have earned him international recognition. he is a JICA Development Studies Fellow and an active member of the Japanese Society of Breeding. he serves as a Topic Coordinator for Frontiers in Plant Science and reviewer for multiple Springer Nature journals, reflecting him scientific leadership and credibility. Him research has received wide acclaim, including recent first-author publications in Nature and Nature Communications. he is frequently invited to contribute to major genomics projects and collaborative research efforts across institutions. Him educational and research fellowships reflect both academic merit and a commitment to global development goals. Through capacity-building roles and farmer-outreach programs, he has further shown a strong drive for science impact beyond academia.

Research Focus:

Dr. Kitony’s research focuses on plant genomics, trait discovery, and sustainable crop improvement. he specializes in GWAS, QTL mapping, pangenomics, transcriptomics, and genomic selection, aiming to uncover the genetic basis of traits related to stress tolerance, yield, and adaptation. he applies high-throughput sequencing (PacBio, ONT, Hi-C) and multi-environment field phenotyping using UAVs, LiDAR, and spectral imaging to support breeding decisions. He work emphasizes integrative multi-omics, applying CRISPR target prioritization and genomic prediction in crops like rice, sorghum, baobab, and cannabis. By connecting computational biology and real-world agriculture, him goal is to develop climate-smart, high-yielding, and biodiversity-supportive seed systems. he also champions open-access data practices, reproducible pipelines, and collaborative research, ensuring him innovations are scalable and impactful across regions, particularly in the Global South.

Publication Top Notes:

  1. Domesticated cannabinoid synthases amid a wild mosaic cannabis pangenome – Nature

  2. Chromosome-level baobab genome illuminates its evolutionary insights – Nature Communications

  3. Nested Association Mapping Population in Crops: Current Status and Future Prospects – J. Crop Sci. Biotech.

  4. Development of an aus-derived Nested Association Mapping (aus-NAM) Population in Rice – Plants

  5. Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites – Scientific Reports

  6. Utilization of genotyping-by-sequencing (GBS) for rice pre-breeding and improvement: A review – Life

  7. Chromosome-level baobab genome illuminates its evolutionary trajectory and environmental adaptation – Nature Communications

  8. Domesticated cannabinoid synthases amid a wild mosaic cannabis pangenome – Nature

  9. Pangenome of US ex-PVP and Wild Sorghum Reveals Structural Variants and Selective Sweeps – bioRxiv

  10. Soil depth determines the microbial communities in Sorghum bicolor fields – Microbiology Spectrum

Conclusion:

Dr. Justine K. Kitony exemplifies the qualities of an outstanding researcher worthy of a Research for Excellence Award. He deep expertise in plant genomics, leadership of high-impact projects, strong publication record, and dedication to mentoring mark him as a leading figure in crop genetics and breeding. While there are areas for growth such as expanding him international and public engagement, these do not detract from him significant scientific contributions. Recognizing Dr. Kitony would not only honor him achievements but also encourage continued innovation in sustainable agriculture, genomic research, and capacity development — fields critical to addressing global challenges related to food security and biodiversity conservation.

Alireza Sharifi | Crop yield prediction | Best Researcher Award

Assoc Prof Dr Alireza Sharifi |  Crop yield prediction |  Best Researcher Award

Dr at  Shahid Rajaee Teacher Training, Iran

Dr. Alireza Sharifi is an Associate Professor at Shahid Rajaee Teacher Training University, specializing in geosciences and remote sensing applications for Precision Agriculture. He obtained his graduate degree from the University of Tehran and has focused his research on Earth Observation Programs, particularly using satellite imagery. Dr. Sharifi has led multiple research projects funded by prestigious organizations such as the National Natural Science Foundation of China, contributing significantly to the fields of hyperspectral image classification, crop mapping, and vegetation monitoring. With numerous publications and a strong academic background, he continues to advance innovative approaches in agricultural sustainability through technology.

Profile:

📚 Academic Background:

Dr. Alireza Sharifi graduated from the University of Tehran with expertise in geosciences and remote sensing applications, focusing on Earth Observation Programs for Precision Agriculture using satellite imagery.

🔍 Research Focus:

His research spans various projects, including hyperspectral image classification, spatio-temporal analysis of forest fires, and integration of satellite images for crop mapping. He has published extensively in reputed journals and holds multiple editorial appointments.

📊 Citations:

  • Citations: 1970 (1878 since 2019)
  • h-index: 28 (since 2019)
  • i10-index: 40 (since 2019)
📄 Publication:

1. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas
S Barati, B Rayegani, M Saati, A Sharifi, M Nasri
The Egyptian Journal of Remote Sensing and Space Science 14 (1), 49-56, 2011
Citations: 221

2. Hyperspectral image classification using a hybrid 3D-2D convolutional neural networks
S Ghaderizadeh, D Abbasi-Moghadam, A Sharifi, N Zhao, A Tariq
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Citations: 132

3. Yield prediction with machine learning algorithms and satellite images
A Sharifi
Journal of the Science of Food and Agriculture 101 (3), 891-896, 2021
Citations: 105

4. Spatio-temporal analysis of forest fire events in the Margalla Hills, Islamabad, Pakistan using socio-economic and environmental variable data with machine learning methods
A Tariq, H Shu, S Siddiqui, I Munir, A Sharifi, Q Li, L Lu
Journal of Forestry Research 33 (1), 183-194, 2022
Citations: 76

5. Crop type classification by DESIS hyperspectral imagery and machine learning algorithms
N Farmonov, K Amankulova, J Szatmári, A Sharifi, D Abbasi-Moghadam, …
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Citations: 62

6. Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models
SS Wahla, JH Kazmi, A Sharifi, SA Shirazi, A Tariq, H Joyell Smith
Geocarto International 37 (27), 14963-14982, 2022
Citations: 62

7. Multiscale dual-branch residual spectral–spatial network with attention for hyperspectral image classification
S Ghaderizadeh, D Abbasi-Moghadam, A Sharifi, A Tariq, S Qin
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Citations: 60

8. Evaluation of vegetation indices and phenological metrics using time-series MODIS data for monitoring vegetation change in Punjab, Pakistan
P Hu, A Sharifi, MN Tahir, A Tariq, L Zhang, F Mumtaz, SHIA Shah
Water 13 (18), 2550, 2021
Citations: 59

9. Flash flood susceptibility assessment and zonation by integrating analytic hierarchy process and frequency ratio model with diverse spatial data
A Tariq, J Yan, B Ghaffar, S Qin, BG Mousa, A Sharifi, ME Huq, M Aslam
Water 14 (19), 3069, 2022
Citations: 57

10. Remote sensing satellite’s attitude control system: rapid performance sizing for passive scan imaging mode
A Kosari, A Sharifi, A Ahmadi, M Khoshsima
Aircraft Engineering and Aerospace Technology 92 (7), 1073-1083, 2020
Citations: 57

11. Flood mapping using relevance vector machine and SAR data: A case study from Aqqala, Iran
A Sharifi
Journal of the Indian Society of Remote Sensing 48 (9), 1289-1296, 2020
Citations: 55

12. Modeling and predicting land use land cover spatiotemporal changes: A case study in Chalus Watershed, Iran
S Jalayer, A Sharifi, D Abbasi-Moghadam, A Tariq, S Qin
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Citations: 54

13. Integration of Sentinel 1 and Sentinel 2 satellite images for crop mapping
S Felegari, A Sharifi, K Moravej, M Amin, A Golchin, A Muzirafuti, A Tariq, …
Applied Sciences 11 (21), 10104, 2021
Citations: 54

14. Development of a method for flood detection based on Sentinel‐1 images and classifier algorithms
A Sharifi
Water and Environment Journal 35 (3), 924-929, 2021
Citations: 54

15. Estimation of forest biomass using multivariate relevance vector regression
A Sharifi, J Amini, R Tateishi
Photogrammetric Engineering & Remote Sensing 82 (1), 41-49, 2016
Citations: 54

16. Agro climatic zoning of saffron culture in Miyaneh city by using WLC method and remote sensing data
A Zamani, A Sharifi, S Felegari, A Tariq, N Zhao
Agriculture 12 (1), 118, 2022
Citations: 50

17. Forest biomass estimation using synthetic aperture radar polarimetric features
A Sharifi, J Amini
Journal of Applied Remote Sensing 9 (1), 097695-097695, 2015
Citations: 49

18. Remotely sensed vegetation indices for crop nutrition mapping
A Sharifi
Journal of the Science of Food and Agriculture 100 (14), 5191-5196, 2020
Citations: 47

19. Using Sentinel-2 data to predict nitrogen uptake in maize crop
A Sharifi
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Citations: 47

20. Speckle reduction of PolSAR images in forest regions using fast ICA algorithm
A Sharifi, J Amini, JT Sri Sumantyo, R Tateishi
Journal of the Indian Society of Remote Sensing 43, 339-346, 2015
Citations: 47