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Digital and Precision Agriculture Lab, University of MarylandPrecision Agriculture Technology Conference on Feb 26, 2026 at the Crowne Plaza, Annapolis.Digital and Precision Agriculture Lab, University of MarylandPrecision Agriculture Technology Conference on Feb 26, 2026 at the Crowne Plaza, Annapolis.

Research Publications

Google ScholarResearchGate

Research Articles

  • Kumar, H., Fleuridor, L., Haden, V.R., Chiavegato, M., Jackson-Smith, D., & Lyon, S. (2025). Soil and water impacts around re-introduction of manure into farming systems. Agrosystems, Geosciences & Environment. DOI
  • Palli, A*. & Kumar, H. (2024). Integration of Smart Drones and AI into Agriculture – A Review. Journal of NACAA, 17. View Article
  • Takhellambam, B. S., Srivastava, P., Lamba, J., Zhao, W., Kumar, H., Tian, D., & Molinari, R. (2024). Artificial neural network-empowered projected future rainfall intensity-duration-frequency curves under changing climate. Atmospheric Research, 297, 107122. DOI9 citations
  • Kumar, H., Miller, S. A., & Lyon, S. W. (2023). Assessing nutrient concentrations and field-scale seepage load under an automated drainage water management system in Ohio. Smart Agricultural Technology, 6, 100328. DOI1 citation
  • Kumar, H., Srivastava, P., Lamba, J., Lena, B., Diamantopoulos, E., Ortiz, B., Takhellambam, B. S., Morata, G., & Bondesan, L. (2023). A methodology to optimize site-specific field capacity and irrigation thresholds. Agricultural Water Management, 286, 108385. DOI9 citations
  • Takhellambam, B. S., Srivastava, P., Lamba, J., McGehee, R. P., Kumar, H., & Tian, D. (2023). Projected mid-century rainfall erosivity under climate change over the southeastern United States. Science of The Total Environment, 865, 161119. DOI19 citations
  • Kumar, H., Srivastava, P., Lamba, J., Diamantopoulos, E., Ortiz, B., Morata, G., Takhellambam, B. S., & Bondesan, L. (2022). Site-specific irrigation scheduling using one-layer soil hydraulic properties and inverse modeling. Agricultural Water Management, 273, 107877. DOI22 citations
  • Kumar, H., Srivastava, P., Lamba, J., Ortiz, B. V., Way, T. R., Sangha, L., Takhellambam, B. S., Morata, G., & Molinari, R. (2022). Within-field variability in nutrients for site-specific agricultural management in irrigated cornfield. Journal of the ASABE, 65(4), 865-880. DOI15 citations
  • Takhellambam, B. S., Srivastava, P., Lamba, J., McGehee, R. P., Kumar, H., & Tian, D. (2022). Temporal disaggregation of hourly precipitation under changing climate over the Southeast United States. Scientific Data, 9(1), 211. DOI23 citations
  • Lena, B. P., Bondesan, L., Pinheiro, E. A. R., Ortiz, B. V., Morata, G. T., & Kumar, H. (2022). Determination of irrigation scheduling thresholds based on HYDRUS-1D simulations of field capacity for multilayered agronomic soils in Alabama, USA. Agricultural Water Management, 259, 107234. DOI22 citations
  • Kumar, H., Srivastava, P., Ortiz, B. V., Morata, G., Takhellambam, B. S., Lamba, J., & Bondesan, L. (2021). Field-scale spatial and temporal soil water variability in irrigated croplands. Transactions of the ASABE, 64(4), 1277-1294. DOI18 citations
  • Ridolfi, E., Kumar, H., & Bárdossy, A. (2020). A methodology to estimate flow duration curves at partially ungauged basins. Hydrology and Earth System Sciences, 24(4), 2043-2060. DOI42 citations
  • Sangha, L., Lamba, J., & Kumar, H. (2020). Effect of ENSO-based upstream water withdrawals for irrigation on downstream water withdrawals. Hydrology Research, 51(4), 602-620. DOI5 citations
  • Sangha, L., Lamba, J., Kumar, H., Srivastava, P., Dougherty, M., & Prasad, R. (2020). An innovative approach to rainwater harvesting for irrigation based on El Niño Southern Oscillation forecasts. Journal of Soil and Water Conservation, 75(5), 565-578. DOI9 citations

Edited Book

  • Himanshu, S.K., Kumar, H., & Gupta, P.K. (under-review). Precision Technologies for digital agriculture: Harnessing IoT, big data, crop modeling, and AI for agricultural production. Elsevier.

Book Chapters

  • Palli, A.*, Sater, H., & Kumar, H. (accepted). Optimizing UAV spray application for pesticide deposition in watermelon production systems in the Mid-Atlantic. Precision Technologies for Digital Agriculture: Harnessing IoT, Big Data, Crop Modeling, and AI for Agricultural Production. Elsevier.
  • Singh, R*. & Kumar, H. (accepted). Next-Gen Data-Driven Precision Irrigation: Application of Artificial Intelligence with In-Situ and Remote Sensing Measurements for Management Zones and VRI Technologies. Precision Technologies for digital agriculture: Harnessing IoT, big data, crop modeling, and AI for agricultural production. Elsevier.
  • Madolli, M., Gade, S., Debnath, S., Kumar, H., & Himanshu, S.K. (accepted). Urbanization, Industrialization, and Water Security: Theoretical perspective and empirical evidence. Precision Technologies for Digital Agriculture: Harnessing IoT, Big Data, Crop Modeling, and AI for Agricultural Production. Elsevier.
  • Williams, N., Grant, K., & Kumar, H. (accepted). Data Sharing, Privacy, and Management Strategies and Security Protocols for Smart Agricultural Systems. Precision Technologies for digital agriculture: Harnessing IoT, big data, crop modeling, and AI for agricultural production. Elsevier.
  • Kumar, H., Ortiz, B.V., Srivastava, P., & Lamba, J. (2024). Assessing nutrient variability in irrigated agricultural fields using unsupervised learning and mixed models. Advances in agri-tech approaches for nutrients and irrigation water management. Taylor and Francis Group, 68-82.

Other Research Activities

  • Quantifying water flow pathway redistribution under agricultural drainage. The Ohio State University, USA. (Ohio Water Resources Center, USGS).
  • Characterization of Water Quality and Quantity of greenhouse or nursery waters in Erie Lake Lowland or Drift Plain Eco-Regions of the USA. The Ohio State University and Application Technology Research Unit, USA. (USDA ARS Cooperative Agreement).
  • Comparing the environmental tradeoffs and synergies of alternative modes of integrating livestock into cash grain cropping systems. The Ohio State University, USA. (National Institute of Food and Agriculture (NIFA) (Agriculture and Food Research Initiative (AFRI). Inter-Disciplinary Engagement in Animal Systems (IDEAS (A1261) program area).
  • Increasing adoption of climate- and water-smart irrigation practices among Tennessee Valley farmers in Alabama and Tennessee. Auburn University, USA. (USDA NRCS CIG 69- 3A75- 17-273).
  • Quantification of nutrient transport dynamics in agricultural landscapes. Auburn University, USA. (USDA NIFA ALA014-0-19052).
  • Estimation of flash floods in ungauged watersheds. Technical University of Munich, Germany. (Bavarian State Ministry of the Environment and Consumer Protection and the Bavarian Environmental Agency).
  • The regional assessment of statistical properties of discharge for Unobserved catchments using meteorological and regional hydrological information. University of Stuttgart, Germany. (DAAD Germany).
  • Hydrological modeling of large catchments using an Approximate Physically Based Model. Indian Institute of Technology, Roorkee, India. (MHRD India).
  • Design refinement, development of solid model and field evaluation of paddy nursery cutter. Punjab Agricultural University, India.