Dr Reddy Pullanagari
Grant-Funded Researcher (C)
School of Agriculture, Food and Wine
College of Science
Eligible to supervise Masters and PhD - email supervisor to discuss availability.
Dr. Reddy was born and raised in Mahabubnagar, Telangana, India, in a family with deep farming roots. His academic journey began at Acharya NG Ranga Agricultural University (now known as Prof. Jayashankar Agricultural University) in Hyderabad, India, where he earned a B.Sc. in Agriculture. This foundational degree equipped him with a thorough understanding of agricultural principles and practices. He then pursued an M.Sc. in Agronomy at the University of Agricultural Sciences, GKVK, Bangalore, India, where he further honed his expertise in crop science and agronomic research.Eager to explore new opportunities, Dr. Reddy migrated to New Zealand, where he enrolled in a Ph.D. program in Precision Agriculture at Massey University. After successfully completing his doctoral studies, he joined Massey University as a scientist, focusing on developing precision agricultural technologies for grasslands and horticultural systems. His work aimed to empower farmers by enhancing food production while reducing environmental impacts, addressing critical challenges in agriculture amid a rapidly evolving global landscape.In 2023, Dr. Reddy embraced a new opportunity as Technology Development Lead with the prestigious Australian Plant Phenomics Network (APPN) in Australia. This role allowed him to expand his horizons by collaborating with leading experts in plant phenotyping, further deepening his understanding of plant responses to environmental stimuli and stressors. Through this transition, Dr. Reddy continues to contribute to cutting-edge research and innovation in sustainable agriculture.
Reddy’s research lies at the intersection of plant phenotyping, precision agriculture, and remote sensing, where he focuses on developing innovative and scalable frameworks to analyze diverse datasets collected from controlled environments to expansive field scales. Leveraging a wide array of sensors—from leaf-level measurements to farm-scale assessments—he specializes in translating complex, multi-dimensional data into actionable insights and plant traits. This work bridges science and application, empowering both advanced breeding programs and precision management strategies.
Central to his approach is the integration of cutting-edge technologies, including machine learning, physics-informed algorithms, and radiative transfer models. These methodologies enable the transformation of raw sensor data into meaningful outputs that address critical challenges in agricultural research and practice.
Currently, as part of an AgriFutures-sponsored project, his work focuses on quantifying oaten quality traits using LiDAR and hyperspectral imaging. Additionally, his research involves actively designing a low-cost phenotyping robot to quantify a variety of plant traits.
| Date | Position | Institution name |
|---|---|---|
| 2023 - ongoing | Grant-Funded Researcher (C), Technology and Development Lead | University of Adelaide |
| 2022 - 2023 | Senior Scientist | Stoneleigh Consulting Limited |
| 2021 - 2022 | Lead Scientist | PlantTech Research Institute |
| 2019 - 2021 | Senior Research Officer | Massey University |
| 2012 - 2019 | Research Officer | Massey University |
| Language | Competency |
|---|---|
| English | Can read, write, speak, understand spoken and peer review |
| Hindi | Can read, write, speak and understand spoken |
| Telugu | Can read, write, speak, understand spoken and peer review |
| Date | Institution name | Country | Title |
|---|---|---|---|
| 2012 | Massey University | New Zealand | PhD |
| Year | Citation |
|---|---|
| 2026 | Adel, A., Pullanagari, R., Alani, N. H. S., Al-Rawi, M., Fouzia, S., & Berger, B. (2026). Drones-of-the-Future in Agriculture 5.0 – Automation, integration, and optimisation. Agricultural Systems, 231, 19 pages. |
| 2025 | Pinna, D., Basso, E., Pornaro, C., Pullanagari, R., Macolino, S., Pezzuolo, A., & Marinello, F. (2025). Optimising grassland Above-Ground biomass Estimation for managed grasslands: A Gaussian process regression approach for Sentinel-2 and Planet Scope in Northern Italy. Precision Agriculture, 26(6), 29 pages. |
| 2025 | Pacheco-Labrador, J., Cendrero-Mateo, M. P., Van Wittenberghe, S., Hernandez-Sequeira, I., Koren, G., Prikaziuk, E., . . . Kopkáně, D. (2025). Ecophysiological variables retrieval and early stress detection: insights from a synthetic spatial scaling exercise. International Journal of Remote Sensing, 46(1), 443-468. Scopus2 WoS2 |
| 2024 | Chakraborty, R., Kereszturi, G., Pullanagari, R., Craw, D., Durance, P., & Ashraf, S. (2024). Inferring arsenic anomalies indirectly using airborne hyperspectral imaging – Implication for gold prospecting along the Rise and Shine Shear Zone in New Zealand. Journal of Geochemical Exploration, 263, 15 pages. |
| 2024 | Dehghan-Shoar, M. H., Kereszturi, G., Pullanagari, R. R., Orsi, A. A., Yule, I. J., & Hanly, J. (2024). A physically informed multi-scale deep neural network for estimating foliar nitrogen concentration in vegetation. International Journal of Applied Earth Observation and Geoinformation, 130, 103917. Scopus11 WoS9 |
| 2023 | Rodriguez-Gomez, C., Kereszturi, G., Whitehead, M., Reeves, R., Rae, A., & Pullanagari, R. (2023). Point pattern analysis of thermal anomalies in geothermal fields and its use for inferring shallow hydrological processes. Geothermics, 110, 102664. Scopus6 WoS4 |
| 2023 | Dehghan-Shoar, M. H., Pullanagari, R. R., Kereszturi, G., Orsi, A. A., Yule, I. J., & Hanly, J. (2023). A Unified Physically Based Method for Monitoring Grassland Nitrogen Concentration with Landsat 7, Landsat 8, and Sentinel-2 Satellite Data. Remote Sensing, 15(10), 21 pages. Scopus17 WoS13 |
| 2023 | Dehghan-Shoar, M. H., Pullanagari, R. R., Orsi, A. A., & Yule, I. J. (2023). Simulating spaceborne imaging to retrieve grassland nitrogen concentration. Remote Sensing Applications: Society and Environment, 29, 100912. Scopus7 WoS6 |
| 2023 | Dehghan-Shoar, M. H., Orsi, A. A., Pullanagari, R. R., & Yule, I. J. (2023). A hybrid model to predict nitrogen concentration in heterogeneous grassland using field spectroscopy. Remote Sensing of Environment, 285, 1-12. Scopus31 WoS25 |
| 2023 | Pullanagari, R. R., & Cavalli, D. (2023). Advances and applications of multivariate statistics and soil-crop sensing to improve nutrient use efficiency and monitor carbon cycling. Nutrient Cycling in Agroecosystems, 127(1), 97-99. Scopus3 WoS3 |
| 2023 | Rodriguez-Gomez, C., Kereszturi, G., Jeyakumar, P., Pullanagari, R., Reeves, R., Rae, A., & Procter, J. N. (2023). Remote exploration and monitoring of geothermal sources: A novel method for foliar element mapping using hyperspectral (VNIR-SWIR) remote sensing. Geothermics, 111, 102716. Scopus11 WoS11 |
| 2022 | Li, M., Pullanagari, R., Yule, I., & East, A. (2022). Segregation of ‘Hayward’ kiwifruit for storage potential using Vis-NIR spectroscopy. Postharvest Biology and Technology, 189, 1-13. Scopus10 WoS9 |
| 2022 | Chakraborty, R., Kereszturi, G., Pullanagari, R., Durance, P., Ashraf, S., & Anderson, C. (2022). Mineral prospecting from biogeochemical and geological information using hyperspectral remote sensing - Feasibility and challenges. Journal of Geochemical Exploration, 232, 1-15. Scopus28 WoS26 |
| 2021 | Zulkifli, Z., Khairunniza-Bejo, S., Muharam, F. M., Yule, I., Pullanagari, R., Dan, L., & Abdulllah, W. N. Z. Z. (2021). Biomass and yield estimation of MR219 and MR220 of paddy varieties using terrestrial laser scanning data. Basrah Journal of Agricultural Sciences, 34(Special issue 1), 54-62. Scopus1 |
| 2021 | Rodriguez-Gomez, C., Kereszturi, G., Reeves, R., Rae, A., Pullanagari, R., Jeyakumar, P., & Procter, J. (2021). Lithological mapping of Waiotapu Geothermal Field (New Zealand) using hyperspectral and thermal remote sensing and ground exploration techniques. Geothermics, 96, 1-15. Scopus32 WoS30 |
| 2021 | Pullanagari, R. R., & Li, M. (2021). Uncertainty assessment for firmness and total soluble solids of sweet cherries using hyperspectral imaging and multivariate statistics. Journal of Food Engineering, 289, 1-11. Scopus71 WoS63 |
| 2021 | Bhatia, N., Pullanagari, R. R., & Cumming, G. S. (2021). Propagation of atmospheric condition parameter uncertainty in measurements of landscape heterogeneity. International Journal of Remote Sensing, 42(21), 8345-8364. Scopus1 WoS1 |
| 2021 | Pullanagari, R. R., Dehghan-Shoar, M., Yule, I. J., & Bhatia, N. (2021). Field spectroscopy of canopy nitrogen concentration in temperate grasslands using a convolutional neural network. Remote Sensing of Environment, 257, 1-15. Scopus103 WoS93 Europe PMC5 |
| 2021 | Ramadhani, F., Pullanagari, R., Kereszturi, G., & Procter, J. (2021). Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics. Remote Sensing, 13(8), 1-21. Scopus17 WoS9 |
| 2020 | Ramadhani, F., Pullanagari, R., Kereszturi, G., & Procter, J. (2020). Automatic Mapping of Rice Growth Stages Using the Integration of SENTINEL-2, MOD13Q1, and SENTINEL-1. Remote Sensing, 12(21), 3613. Scopus57 WoS44 |
| 2020 | Garhwal, A. S., Pullanagari, R. R., Li, M., Reis, M. M., & Archer, R. (2020). Hyperspectral imaging for identification of Zebra Chip disease in potatoes. Biosystems Engineering, 197, 306-317. Scopus37 WoS35 Europe PMC6 |
| 2020 | Ramadhani, F., Pullanagari, R., Kereszturi, G., & Procter, J. (2020). Mapping of rice growth phases and bare land using Landsat-8 OLI with machine learning. International Journal of Remote Sensing, 41(21), 8428-8452. Scopus30 WoS23 |
| 2018 | Kereszturi, G., Schaefer, L. N., Schleiffarth, W. K., Procter, J., Pullanagari, R. R., Mead, S., & Kennedy, B. (2018). Integrating airborne hyperspectral imagery and LiDAR for volcano mapping and monitoring through image classification. International Journal of Applied Earth Observation and Geoinformation, 73, 323-339. Scopus61 WoS56 |
| 2018 | Pullanagari, R., Kereszturi, G., & Yule, I. (2018). Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression. Remote Sensing, 10(7), 1-14. Scopus130 WoS112 |
| 2017 | Pullanagari, R. R., Kereszturi, G., & Yule, I. J. (2017). Quantification of dead vegetation fraction in mixed pastures using AisaFENIX imaging spectroscopy data. International Journal of Applied Earth Observation and Geoinformation, 58, 26-35. Scopus20 WoS19 |
| 2017 | Li, M., Pullanagari, R. R., Pranamornkith, T., Yule, I. J., & East, A. R. (2017). Quantitative prediction of post storage ‘Hayward’ kiwifruit attributes using at harvest Vis-NIR spectroscopy. Journal of Food Engineering, 202, 46-55. Scopus61 WoS50 Europe PMC4 |
| 2017 | Li, M., Pullanagari, R. R., Pranamornkith, T., Yule, I. J., & East, A. R. (2017). Applying visible-near infrared (Vis-NIR) spectroscopy to classify 'Hayward' kiwifruit firmness after storage. Acta Horticulturae, 1154(1154), 1-7. Scopus1 WoS2 |
| 2017 | Pullanagari, R., Kereszturi, G., Yule, I. J., & Ghamisi, P. (2017). Assessing the performance of multiple spectral-spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network. Journal of Applied Remote Sensing, 11(2), 22 pages. Scopus27 WoS24 |
| 2016 | Pullanagari, R. R., Kereszturi, G., & Yule, I. J. (2016). Mapping of macro and micro nutrients of mixed pastures using airborne AisaFENIX hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 1-10. Scopus84 WoS81 |
| 2015 | Pullanagari, R. R., Yule, I. J., & Agnew, M. (2015). Corrigendum to "On-line prediction of lamb fatty acid composition by visible near infrared spectroscopy" [Meat Science 100 (2014) 156-163]. Meat Science, 105, 136. Scopus1 |
| 2015 | Pullanagari, R. R., Yule, I. J., & Agnew, M. (2015). On-line prediction of lamb fatty acid composition by visible near infrared spectroscopy. Meat Science, 100, 156-163. Scopus57 WoS46 Europe PMC22 |
| 2014 | Kim, I., Pullanagari, R. R., Deurer, M., Singh, R., Huh, K. Y., & Clothier, B. E. (2014). The use of visible and near-infrared spectroscopy for the analysis of soil water repellency. European Journal of Soil Science, 65(3), 360-368. Scopus15 WoS15 Europe PMC2 |
| 2013 | Pullanagari, R. R., Yule, I. J., Tuohy, M. P., Hedley, M. J., Dynes, R. A., & King, W. M. (2013). Proximal sensing of the seasonal variability of pasture nutritive value using multispectral radiometry. Grass and Forage Science, 68(1), 110-119. Scopus35 WoS33 |
| 2012 | Pullanagari, R. R., Yule, I. J., Hedley, M. J., Tuohy, M. P., Dynes, R. A., & King, W. M. (2012). Multi-spectral radiometry to estimate pasture quality components. Precision Agriculture, 13(4), 442-456. Scopus26 WoS25 Europe PMC2 |
| 2012 | Pullanagari, R. R., Yule, I. J., Tuohy, M. P., Hedley, M. J., Dynes, R. A., & King, W. M. (2012). In-field hyperspectral proximal sensing for estimating quality parameters of mixed pasture. Precision Agriculture, 13(3), 351-369. Scopus115 WoS99 Europe PMC8 |
| 2011 | Pullanagari, R. R., Yule, I., King, W., Dalley, D., & Dynes, R. (2011). The use of optical sensors to estimate pasture quality. International Journal on Smart Sensing and Intelligent Systems, 4(1), 125-137. Scopus28 WoS28 |
| - | Truong, H. T. D., Al-Sarayreh, M., Pullanagari, R., Reis, M. M., & Archer, R. (n.d.). The Potential of Deep Learning to Counter the Matrix Effect for Assessment of Honey Quality and Monoflorality. |
| Year | Citation |
|---|---|
| 2012 | Yule, I., & Pullanagari, R. (2012). Optical sensors to assist agricultural crop and pasture management. In Lecture Notes in Electrical Engineering (Vol. 146 LNEE, pp. 21-32). Springer Berlin Heidelberg. DOI Scopus5 |
| Year | Citation |
|---|---|
| 2024 | Hossain, D. S. M., Pullanagari, R. R., Alvaro, O., & Yule Ian, J. (2024). Extraction of solar-induced fluorescence (SIF) from airborne hyperspectral data. In 2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024 (pp. 1-3). Online: IEEE. DOI |
| 2024 | Hossain, D. S. M., Gabor, K., Pullanagari, R. R., Yule Ian, J., Alvaro, O., & James, H. (2024). Multi-scale estimation of vegetation nitrogen concentration using a physically informed neural network. In 2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024 (pp. 1-3). Online: IEEE. DOI |
| 2021 | Rodriguez-Gomez, C., Kereszturi, G., Reeves, R., Mead, S., Pullanagari, R., Rae, A., & Jeyakumar, P. (2021). Mapping Antimony Concentration over Geothermal Areas Using Hyperspectral and Thermal Remote Sensing. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 1086-1089). Online: IEEE. DOI Scopus1 WoS1 |
| 2021 | Chakraborty, R., Kereszturi, G., Durance, P., Pullanagari, R., Ashraf, S., & Anderson, C. (2021). Biogeochemical Exploration of Gold Mineralization and its Pathfinder Elements Using Hyperspectral Remote Sensing. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5119-5122). Online: IEEE. DOI Scopus3 WoS3 |
| 2018 | Pullanagari, R. R., Kereszturi, G., Yule, I. J., & Irwin, M. (2018). Determining uncertainty prediction map of copper concentration in pasture from hyperspectral data using qunatile regression forest. In 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings Vol. 2018-July (pp. 3809-3811). New Jersey, USA: IEEE. DOI Scopus5 WoS4 |
| 2018 | Kereszturi, G., Pullanagari, R. R., Mead, S., Schaefer, L. N., Procter, J., Schleiffarth, W. K., & Kennedy, B. (2018). Geological mapping of hydrothermal alteration on volcanoes from multi-sensor platforms. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium Vol. 2018-July (pp. 220-223). New Jersey, USA: IEEE. DOI Scopus6 WoS4 |
| 2016 | Yule, I. J., Pullanagari, R. R., & Kereszturi, G. (2016). Detecting subtle environmental change: A multi-Temporal airborne imaging spectroscopy approach. In C. M. U. Neale, & A. Maltese (Eds.), Proceedings of SPIE the International Society for Optical Engineering Vol. 9998 (pp. 7 pages). Edinburgh, SCOTLAND: SPIE-INT SOC OPTICAL ENGINEERING. DOI Scopus1 |
| 2015 | Pullanagari, R. R., Kereszturi, G., Yule, I. J., & Irwin, M. E. (2015). Determination of pasture quality using airborne hyperspectral imaging. In C. M. U. Neale, & A. Maltese (Eds.), Proceedings of SPIE the International Society for Optical Engineering Vol. 9637 (pp. 5 pages). Toulouse, FRANCE: SPIE-INT SOC OPTICAL ENGINEERING. DOI Scopus1 WoS1 |
Pullanagari R.R. , Rattey, A., Ganesalingam, D., Berger, B. and M., Hennekam (2024-2027). Dispelling value uncertainty for hay growers – application of high-throughput, low-cost phenomics to predict Oaten hay quality in-crop fields, while improving oat breeding selection efficiency, Funded by AgriFutures Australia.
Recently finished Grants:
A 3D model of radiation transport to enable high yield photosynthetic efficient crops (2021-23) Link, PI, Funded by MBIE, New Zealand (≈ 1 million NZD).
Pioneering to Precision (2012-22), PGP, Application of Fertiliser in Hill Country Link, Research Champion, Funded by Ministry of Primary Industries and Ravensdown, New Zealand (>11 million NZD).
Mapping fruit quality of Kiwifruit, Apple, and Avocado orchards using airborne remote sensing. 2020-2022, PI, Funded by Plantech Research Institute and MBIE, New Zealand.
Modelling pasture data at farm-scale (2021-2023) Link, PI, Funded by MPI, NZ (NZD 299,902).
| Date | Role | Research Topic | Program | Degree Type | Student Load | Student Name |
|---|---|---|---|---|---|---|
| 2025 | Co-Supervisor | Novel Field-based Phenotyping Methods for Trait Evaluation | Doctor of Philosophy | Doctorate | Full Time | Mr Qiwei Shen |
Connect With Me
External Profiles
