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Dharmendra Saraswat
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A recommended calibration and validation strategy for hydrologic and water quality models
P Daggupati, N Pai, S Ale, KR Douglas-Mankin, RW Zeckoski, J Jeong, ...
Transactions of the ASABE 58 (6), 1705-1719, 2015
2452015
A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools
A Ahmad, D Saraswat, A El Gamal
Smart Agricultural Technology 3, 100083, 2023
1062023
SWAT2009_LUC: A tool to activate the land use change module in SWAT 2009
N Pai, D Saraswat
Transactions of the ASABE 54 (5), 1649-1658, 2011
1012011
Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems
A Ahmad, D Saraswat, V Aggarwal, A Etienne, B Hancock
Computers and Electronics in Agriculture 184, 106081, 2021
982021
Hydrologic and water quality models: Key calibration and validation topics
DN Moriasi, RW Zeckoski, JG Arnold, C Baffaut, RW Malone, P Daggupati, ...
Transactions of the ASABE 58 (6), 1609-1618, 2015
862015
Smartphone-based hierarchical crowdsourcing for weed identification
M Rahman, B Blackwell, N Banerjee, D Saraswat
Computers and Electronics in Agriculture 113, 14-23, 2015
582015
Identifying priority subwatersheds in the Illinois River drainage area in Arkansas watershed using a distributed modeling approach
N Pai, D Saraswat, M Daniels
Transactions of the ASABE 54 (6), 2181-2196, 2011
462011
Field_SWAT: A tool for mapping SWAT output to field boundaries
N Pai, D Saraswat, R Srinivasan
Computers & Geosciences 40, 175-184, 2012
442012
A two-stage deep-learning based segmentation model for crop disease quantification based on corn field imagery
LG Divyanth, A Ahmad, D Saraswat
Smart Agricultural Technology 3, 100108, 2023
432023
Development and evaluation of targeted marginal land mapping approach in SWAT model for simulating water quality impacts of selected second generation biofeedstock
G Singh, D Saraswat
Environmental modelling & software 81, 26-39, 2016
422016
Biofuels and water quality: challenges and opportunities for simulation modeling
B Engel, I Chaubey, M Thomas, D Saraswat, P Murphy, B Bhaduri
Biofuels 1 (3), 463-477, 2010
352010
Deep learning-based object detection system for identifying weeds using uas imagery
A Etienne, A Ahmad, V Aggarwal, D Saraswat
Remote Sensing 13 (24), 5182, 2021
322021
The impact of temperature and shallow hydrologic conditions on the magnitude and spatial pattern consistency of electromagnetic induction measured soil electrical conductivity.
BJ Allred, MR Ehsani, D Saraswat
322005
Machine learning approaches to automate weed detection by UAV based sensors
A Etienne, D Saraswat
Autonomous air and ground sensing systems for agricultural optimization and …, 2019
312019
Hydrologic and water quality models: Documentation and reporting procedures for calibration, validation, and use
D Saraswat, JR Frankenberg, N Pai, S Ale, P Daggupati, ...
Transactions of the ASABE 58 (6), 1787-1797, 2015
312015
Comparison of electromagnetic induction, capacitively-coupled resistivity, and galvanic contact resistivity methods for soil electrical conductivity measurement.
BJ Allred, MR Ehsani, D Saraswat
302006
A sensitivity analysis of impacts of conservation practices on water quality in L’Anguille River Watershed, Arkansas
G Singh, D Saraswat, A Sharpley
Water 10 (4), 443, 2018
272018
CD&S dataset: Handheld imagery dataset acquired under field conditions for corn disease identification and severity estimation
A Ahmad, D Saraswat, AE Gamal, G Johal
arXiv preprint arXiv:2110.12084, 2021
242021
Toward generalization of deep learning-based plant disease identification under controlled and field conditions
A Ahmad, A El Gamal, D Saraswat
IEEE Access 11, 9042-9057, 2023
232023
Design and operational parameters of a pneumatic seed metering device for planting of groundnut (Arachis hypogaea) seeds.
RC Singh, G Singh, DC Saraswat
222007
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