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My name is Amninder Singh.
And I am a Data Scientist

More About Me Projects

About

More About Me

With a growing population, water shortages, and climate change impacts, it is clear that we need to keep working to find solutions that make agriculture more efficient. The efficiencies enabled by the advancement of digital technologies can help us get more out of agriculture while conserving our natural resources. I am a researcher passionate about using earth observatory data, geophysical measurements, and machine learning approaches to address these challenges.

Education

Ph.D., Environmental Sciences (Soil &Water)

MS, Plant Science

BS, Agriculture

Skills

Python

I have worked on various machine learning projects using agricultural datasets. Experience working with libraries such as TensorFlow, pandas, NumPy, matplotlib, seaborn, etc.

Geospatial data analysis

Proficient in analyzing satellite/drone imagery using platforms like ArcGIS, QGIS, and Google Earth Engine. Python libraries: Geopandas, Rasterio, xarray

Other Skills Matlab R Soil and environmental sensor data acquisition systems Machine Learning Data visualization SQL Git Remote Sensing Deep Learning

Download My CV

Work Experience.

Working as a Data Scientist, Remote Sensing. Climate LLC (DFS) is the digital farming arm of Bayer Crop Science.

I worked as a contractor with the Data Insights Geospatial and Remote Sensing team. Responsible for fulfilling requests for geospatial data analysis from various teams and creating and presenting the workflow to stakeholders.

The focus of my work was to study high-resolution soil-plant relationships. A particular focus was on the estimation of soil moisture by combining public and private soil moisture ground measurements, remote sensing, and other datasets with machine learning methods

Dissertation: 'Advancing Urban Landscape Irrigation Management using Smart Controllers and Machine Learning-Based Models'. Worked on a variety of projects related to irrigation and water management using different approaches, such as advanced data acquisition, machine learning, remote sensing, and GIS & GPS technologies. I was responsible for aggregating & statistically analyzing large agricultural datasets using Python, R or MATLAB

Thesis: 'Use of EM-38 soil surveys in forage fields at a saline drainage water reuse site to calibrate a hydro-salinity model for decision support.' I was responsible for conducting soil salinity mapping using electromagnetic sensing platforms and analyzing spatial data.

Projects

Soil Moisture Estimation of Agricultural Fields using Remote Sensing

Autonomous landscape water conservation strategies using smart irrigation controllers

Temperature-based empirical and ML ET models

Artificial Neural Network (ANN) based Pedotransfer functions

Electromagnetic Induction (EM-38) for mapping soil salinity

Publications

Singh, A., Verdi, A., 2024. Estimating the soil water retention curve by the HYPROP-WP4C system, HYPROP-based PCNN-PTF and inverse modeling using HYDRUS-1D. Journal of Hydrology 639, 131657. https://doi.org/10.1016/j.jhydrol.2024.131657

Singh, A., Verdi, A., Haver, D., Sapkota, A., Iradukunda, J. C., 2024. Using a soil moisture sensor-based smart controller for autonomous irrigation management of hybrid bermudagrass with recycled water in coastal Southern California. Agricultural Water Management 299, 108906. https://doi.org/10.1016/j.agwat.2024.108906

Verdi, A., Singh, A., Sapkota, A., Ghodsi, S., 2024. Assessing the Impact of Water Conservation on Cooling Potential of Two Turfgrass Species. Journal of the ASABE. 67(3): 749-759. https://doi.org/10.13031/ja.15845

Singh, A., Haghverdi, A., 2023. Development and evaluation of temperature-based deep learning models to estimate reference evapotranspiration. Artificial Intelligence in Agriculture. https://doi.org/10.1016/j.aiia.2023.08.003

Sapkota, A., Haghverdi, A., Merhaut, D., Singh, A., & Iradukunda, J. C., 2023. Response of Landscape Groundcovers to Deficit Irrigation: An Assessment Based on Normalized Difference Vegetation Index and Visual Quality Rating. HortScience, 58(3), 274-285. Available at https://doi.org/10.21273/HORTSCI16915- 22.

Haghverdi, A., Reiter,M., Singh, A., and Sapkota, A. 2021. Hybrid Bermudagrass and Tall Fescue Turfgrass Irrigation in Central California: II. Assessment of NDVI, CWSI, and Canopy Temperature Dynamics. Agronomy 11(9): 1733 Available at https://www.mdpi.com/2073-4395/11/9/1733.

Haghverdi, A., Reiter, M., Sapkota, A., and Singh, A. 2021. Hybrid Bermudagrass and Tall Fescue Turfgrass Irrigation in Central California: I. Assessment of Visual Quality, Soil Moisture and Performance of an ETBased Smart Controller. Agronomy 11(8): 1666. Available at https://doi.org/10.3390/agronomy11081666.

Singh, A., Haghverdi, A., Öztürk, H.S., and Durner, W. 2021. Developing Pseudo Continuous Pedotransfer Functions for International Soils Measured with the Evaporation Method and the HYPROP System: II. The Soil Hydraulic Conductivity Curve. Water 13(6): 878 Available at https://www.mdpi.com/2073- 4441/13/6/878.

Haghverdi, A., Singh, A., Sapkota, A., Reiter, M., and Ghodsi, S. 2021. Developing irrigation water conservation strategies for hybrid bermudagrass using an evapotranspiration-based smart irrigation controller in inland southern California. Agric. Water Manag. 245(April 2020): 106586 Available at https://doi.org/10.1016/j.agwat.2020.106586.

Singh, A., Haghverdi, A., Öztürk, H.S., and Durner, W. 2020. Developing pseudo continuous pedotransfer functions for international soils measured with the evaporation method and the hyprop system: I. the soil water retention curve. Water (Switzerland) 12(12): 1–17 Available at https://www.mdpi.com/2073- 4441/12/12/3425.

Singh, A., Quinn, N.W.T., Benes, S.E., and Cassel, F. 2020. Policy-Driven Sustainable Saline Drainage Disposal and Forage Production in the Western San Joaquin Valley of California. Sustain. 2020, Vol. 12, Page 6362 12(16): 6362 Available at https://www.mdpi.com/2071-1050/12/16/6362/htm

Singh, A., Haghverdi, A., Nemati, M., and Hartin, J. 2020. Efficient Urban Water Management Smart Weather-Based Irrigation Controllers. Univ. Calif. Agric. Nat. Resour. 8674(July): 1–11