Reddy Pullanagari

Dr Reddy Pullanagari

Grant-Funded Researcher (C)

School of Agriculture, Food and Wine

Faculty of Sciences, Engineering and Technology

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.

  • Journals
    Year Citation
    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.
    DOI
    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.
    DOI
    2024 Assaf, M. N., Manenti, S., Creaco, E., Giudicianni, C., Tamellini, L., & Todeschini, S. (2024). New optimization strategies for SWMM modeling of stormwater quality applications in urban area. Journal of Environmental Management, 361, 15 pages.
    DOI Scopus10
    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.
    DOI Scopus3
    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.
    DOI Scopus6
    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.
    DOI Scopus11
    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.
    DOI Scopus4 WoS1
    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.
    DOI Scopus28 WoS4
    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.
    DOI Scopus3
    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.
    DOI Scopus8
    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.
    DOI Scopus8 WoS3
    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.
    DOI Scopus23 WoS9
    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.
    DOI 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.
    DOI Scopus29 WoS14
    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.
    DOI Scopus64 WoS34
    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.
    DOI Scopus1
    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.
    DOI Scopus89 WoS44
    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.
    DOI Scopus13 WoS2
    2021 Tian, D., Wang, M., Wang, J., Tu, J., & Jiao, S. (2021). Electrochemical Behaviors of Consumable Ti<inf>2</inf>CO@Al<inf>2</inf>O<inf>3</inf>Anode for Ti Extraction by USTB Process. Journal of the Electrochemical Society, 168(10), 7 pages.
    DOI Scopus3
    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.
    DOI Scopus47 WoS21
    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.
    DOI Scopus36 WoS19
    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.
    DOI Scopus26 WoS12
    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.
    DOI Scopus58 WoS43
    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.
    DOI Scopus117 WoS75
    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.
    DOI Scopus20 WoS15
    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.
    DOI Scopus60 WoS37 Europe PMC3
    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.
    DOI Scopus1 WoS1
    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.
    DOI Scopus26 WoS21
    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.
    DOI Scopus78 WoS59
    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.
    DOI 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.
    DOI Scopus56 WoS42 Europe PMC12
    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.
    DOI Scopus15 WoS14 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.
    DOI Scopus35 WoS30
    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.
    DOI Scopus23 WoS19
    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.
    DOI Scopus110 WoS86
    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.
    DOI Scopus27 WoS21
    - 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.

  • Book Chapters
    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

  • Conference Papers
    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
    2023 Molina, B. G., Bayar, E., Love, R. L., Merrick, A. V., Riani-El-Achhab, S., Lee, Y. S., . . . Sykes, L. (2023). Cervical Shortening is Associated with Cervical Neutrophil Migration and Complement Activation. In REPRODUCTIVE SCIENCES Vol. 30 (pp. 94A-95A). AUSTRALIA, Brisbane: SPRINGER HEIDELBERG.
    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
    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 Scopus2 WoS1
    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 WoS2
    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
    2006 Arnold, J. (2006). Early transition metal chemistry with new multidentate ligands. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY Vol. 231 (pp. 1 page). GA, Atlanta: AMER CHEMICAL SOC.
    2006 Westmoreland, I., & Arnold, J. (2006). Phenoxytriamine complexes of yttrium:: Synthesis, structure and use in the polymerization of lactide and ε-caprolactone. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY Vol. 231 (pp. 2 pages). GA, Atlanta: AMER CHEMICAL SOC.

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).

  • Position: Grant-Funded Researcher (C)
  • Phone: 83133151
  • Email: reddy.pullanagari@adelaide.edu.au
  • Campus: Waite
  • Building: Plant Accelerator, floor Level One
  • Room: 137
  • Org Unit: School of Agriculture, Food and Wine
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