Towards Generalization of Deep Learning-Based Plant Disease Identification Under Controlled and Field Conditions
Ahmad, A.1, Saraswat, D., and El Gamal, A. (2023). Towards Generalization of Deep Learning-Based Plant Disease Identification Under Controlled and Field Conditions. IEEE Access, https://ieeexplore.ieee.org/document/10026479 (Tier 1)
Abstract: Identifying corn diseases under field conditions is crucial for implementing effective disease management systems. Deep learning (DL)-based plant disease identification ...
Flash Drought: Review of Concept, Prediction, and the Potential for Machine Learning, Deep Learning Methods
Tyagi, S.1, Zhang, X., Saraswat, D., Sahany, S., and Niyogi, D. (2022). Flash Drought: Review of Concept, Prediction, and the Potential for Machine Learning, Deep Learning Methods. Earth’s Future, 10(11), https://doi.org/10.1029/2022EF002723 (Tier 1)
Abstract: This paper reviews the Flash Drought concept, the uncertainties associated with FD prediction, and the potential of Machine Learning (ML) and Deep learning (DL) for future applications ...
GeoDLS: A Deep Learning-Based Corn Disease Tracking and Location System Using RTK Geolocated UAS Imagery
Ahmad, A.1, Aggarwal, V.1, Saraswat, D., El Gamal, A., and Johal, G. S. (2022). GeoDLS: A Deep Learning-Based Corn Disease Tracking and Location System Using RTK Geolocated UAS Imagery. Remote Sensing, 14(17), 4140. https://doi.org/10.3390/rs14174140 (Tier 1)
Abstract: Deep learning-based solutions for precision agriculture have recently achieved promising results. Deep learning has been used to identify crop diseases at the initial stages of ...
Comparison of Deep Learning Models for Corn Disease Region Location, Identification of Disease Type, and Severity Estimation Using Images Acquired from UAV-Mounted and Handheld Sensors
Ahmad, A.1, Saraswat, D., Gamal, A.E., and Johal, G. (2022). Comparison of Deep Learning Models for Corn Disease Region Location, Identification of Disease Type, and Severity Estimation Using Images Acquired from UAV-Mounted and Handheld Sensors. Journal of the ASABE. 65(6): 1433-1442. doi: 10.13031/ja.14895 (Tier 1)
Abstract: Accurately locating diseased regions, identifying disease types, and estimating disease severity in corn fields are all connected steps for developing an effective disease management system ...
Performance of Deep Learning Models for Classifying and Detecting Common weeds in Corn and Soybean Production Systems
Ahmad, A1, Saraswat, D., Etienne, A.1, Aggarwal, V.1, and Hancock, B.3(2021). Performance of Deep Learning Models for Classifying and Detecting Common weeds in Corn and Soybean Production Systems. Computers and Electronics in Agriculture, 184. https://doi.org/10.1016/j.compag.2021.106081 (Tier 1)
Abstract: Knowing precise location and having accurate information about weed species is a prerequisite for developing an effective site-specific weed management (SSWM) system ...
LUU Checker- A web-based framework for dynamic accounting of temporal changes in landscape for SWAT model
Singh, G.1, Saraswat, D., Pai, N. 2 and Hancock, B.3 (2019). LUU Checker- A web-based framework for dynamic accounting of temporal changes in landscape for SWAT model. Environmental Modelling & Software. Applied Engineering in Agriculture. 35(5): 723-731. (doi: 10.13031/aea.13295) (Tier 1)
Abstract: Standard practice of setting up Soil and Water Assessment Tool (SWAT) involves use of a single land use (LU) layer under the assumption that no change takes place in LU condition ...
A Survey on Using Deep Learning Techniques for Plant Disease Diagnosis and Recommendations for Development of Appropriate Tools
Ahmad, A.1, Saraswat, D., and El Gamal, A. (2023). A Survey on Using Deep Learning Techniques for Plant Disease Diagnosis and Recommendations for Development of Appropriate Tools. Smart Agricultural Technology, 3, 100083, ISSN 2772-3755, https://doi.org/10.1016/j.atech.2022.100083 (Tier 1)
Abstract: Several factors associated with disease diagnosis in plants using deep learning techniques must be considered to develop a robust system for accurate disease management ...
Identification of Foliar Disease Regions on Corn Leaves Using SLIC Segmentation and Deep Learning Under Uniform Background and Field Conditions
Phan, H.4, Ahmad, A.1, and Saraswat, D. (2022). Identification of Foliar Disease Regions on Corn Leaves Using SLIC Segmentation and Deep Learning Under Uniform Background and Field Conditions. IEEE Access, 10, https://doi.org/10.1109/ACCESS.2022.3215497 (Tier 1)
Abstract: Plant diseases lead to severe losses in crop yield worldwide. The conventional approach for diagnosing diseases relies on manual scouting. In recent years, advances in ...
A Two-Stage Deep-Learning Based Segmentation Model for Crop Disease Quantification Based on Corn Field Imagery
Divyanth, L.G.4, Ahmad, A.1, and Saraswat, D., (2022). A Two-Stage Deep-Learning Based Segmentation Model for Crop Disease Quantification Based on Corn Field Imagery. Smart Agricultural Technology, 3, 100108. https://doi.org/10.1016/j.atech.2022.100108 (Tier 1)
Abstract: It is important to develop accurate disease management systems to identify and segment corn disease lesions and estimate their severity under complex field conditions ...
Deep learning-based object detection system for identifying weeds using UAS imagery
Etienne, A.1, Ahmad, A.1, Aggarwal, V.1, and Saraswat, D. (2021). Deep learning-based object detection system for identifying weeds using UAS imagery. Remote Sensing, 13(24), 5182; https://doi.org/10.3390/rs13245182 (Tier 1)
Abstract: Knowing precise location and having accurate information about weed species is a prerequisite for developing an effective site-specific weed management (SSWM) system ...
Comparative Analysis of Bioenergy Crop Impacts on Water Quality Using Static and Dynamic Land Use Change Modeling Approach
Kumar, E.1, Saraswat, D. and Singh, G.1(2020). Comparative Analysis of Bioenergy Crop Impacts on Water Quality Using Static and Dynamic Land Use Change Modeling Approach, Water, 12(2):40. (Tier 1)
Abstract: Researchers and federal and state agency officials have long been interested in evaluating location-specific impact of bioenergy energy crops on water quality for developing policy interventions ...
A Sensitivity Analysis of Impacts of Conservation Practices on Water Quality in L’Anguille River Watershed, Arkansas
Singh, G. and D. Saraswat. 2018. A Sensitivity
Analysis of Impacts of Conservation Practices on
Water Quality in L’Anguille River Watershed, Arkansas. Agricultural Water Management, Water, 10(4): 443.
Abstract: Assessing the performance of appropriate agricultural conservation practices (CPs) frequently relies on the use of simulation models as a cost-effective tool instead of depending solely...
Evaluating Remotely Sensed Plant Count Accuracy with Differing UAS Altitudes, Differing Canopy Separations and Ground Covers
Leiva, J.N., J. Robbins, D. Saraswat, Y. She and R. Ehsani. 2017. Evaluating Remotely Sensed Plant Count Accuracy with Differing UAS Altitudes, Differing Canopy Separations and Ground Covers. Journal of Applied Remote Sensing: CIGR Journal. 11(3):036003-1 to 036003-15.
Abstract: This study evaluated the effect of flight altitude and canopy separation of containergrown Fire Chief™ arborvitae (Thuja occidentalis L.) on counting accuracy. Images were taken...
Effect of plant canopy shape and flowers on plant count accuracy using remote sensing imagery
Leiva, J.N., J. Robbins, D. Saraswat, Y. She and R. Ehsani. 2016. Effect of Plant Canopy Shape and Flowers on Plant Count Accuracy Using Remote Sensing Imagery. Agricultural Engineering International: CIGR Journal. 18(2):73-82.
Abstract: Separate experiments were conducted to evaluate the effect of plant canopy shape and presence of flowers on counting accuracy of container-grown plants. Images were taken at 12 m above the ground. Two species of juniper...
Development and evaluation of targeted marginal land mapping approach in SWAT model for simulating water quality impacts of selected second generation biofeedstock
Singh, G. and D. Saraswat. 2016. Development and Evaluation of Targeted Land Mapping Approach in SWAT Model for Simulating Water Quality Impacts of Selected Second Generation Biofeedstock. Environmental Modelling and Software. 81: 26-39.
Abstract: Information about location of marginal lands in a watershed is of interest to those who view these areas as potential land for producing biofuel crops. However, representing marginal lands into a distributed...
Post-Model Validation of a Deterministic Watershed Model Using Measured Data
McCarty, J.A., B.E. Haggard, M. Matlock, N. Pai, and D. Saraswat. 2016. Post-Model Validation of a Deterministic Watershed Model Using Measured Data. Trans. ASABE. 59(2): 497-508.
Abstract: Data are often collected during or after hydrological and water quality (H/WQ) model development, thus limiting the ability for direct comparison or use in calibration and validation. In this study, we demonstrate a way to vali...
Smartphone based Hierarchical Crowdsourcing for Weed Identification
Rahman, M., B. Blackwell, N., Banerjee, and D. Saraswat. 2015. Smartphone based Hierarchical Crowdsourcing for Weed Identification. Computers and Electronics in Agriculture 113: 14-23.
Abstract: Weed infestation is a common problem in agriculture that adversely affects crop production. Given severe constraints on the budget of many land-grant universities due to the economic downturn, exten...
Hydrology and Water Quality Models: Documentation and Reporting Procedures for Calibration, Validation, and Use
Saraswat, D., J.R. Frankenberger, N. Pai, S. Ale, P. Daggupati, K.R. Douglas-Mankin, and M.A. Youseff. 2015. Hydrology and Water Quality Models: Documentation and Reporting Procedures for Calibration, Validation, and Use. Trans. ASABE. 58(6): 1787-1797.
Abstract: The increasing use of hydrologic and water quality (H/WQ) models for technical, policy, and legal decision making calls for greater transparency in communicating the methods used and decisions made when using H/WQ models.
A Recommended Calibration and Validation Strategy for Hydrologic and Water Quality Models
Daggupati, P., N. Pai,S. Ale, K.R. Douglas-Mankin, R. W. Zeckoski, J. Jeong, P.B. Parajuli, D. Saraswat, M.A. Youseff. 2015. A Recommended Calibration and Validation Strategy for Hydrologic and Water Quality Models. Trans. ASABE. 58)6): 1705-1719.
Abstract: Hydrologic and water quality (H/WQ) models are widely used to support site-specific environmental assessment, design, planning, and decision making. Calibration and validation (C/V) are fundamental processes used to demon...
Hydrologic and Water Quality Models: Key Calibration and Validation Topics
Moriasi, D., R. W. Zeckoski,J.G. Arnold, C.B. Baffaut, R.W. Malone, P. Daggupati, J.A. Guzman, Y. Yuan, D. Saraswat, B.W. Wilson, and A. Shirmohammadi. 2015. Hydrologic and Water Quality Models: Key Calibration and Validation Topics. Trans. ASABE. 58(6): 1609-1618.
Abstract: As a continuation of efforts to provide a common background and platform for development of calibration and validation (C/V) guidelines for hydrologic and water quality (H/WQ) modeling, ASABE members worked to determine...
Impact of Land Use and Land Cover Categorical Uncertainty on SWAT Hydrologic Modeling
Pai, N and D. Saraswat. 2013. Impact of Land Use and Land Cover Categorical Uncertainty on SWAT Hydrologic Modeling. Trans. ASABE. 56(4): 1387-1397.
Abstract: The land use and land cover (LULC) map of a watershed is a critical input to the Soil and Water Assessment Tool (SWAT) model. LULC is a categorical geospatial data layer that is typically developed based on models that...
A Geospatial Tool for Delineating Streambanks
Pai, N. and D. Saraswat. 2012. A Geospatial Tool for Delineating Streambanks. Environmental Modeling and Software, 40(2013), 151-159.
Abstract: Ecologists, civil engineers, and conservation managers currently lack an automated software tool for delineating streambanks, which is an important input for various hydrological studies. Therefore...
Field_SWAT: A Tool for Mapping SWAT Output to Field Boundaries
Pai, N., D. Saraswat, and R.Srinivasan 2011. Field_SWAT: A Tool for Mapping SWAT Output to Field Boundaries. Computers and Geosciences.
Abstract: The Soil and Water Assessment Tool (SWAT) hydrological/water quality model divides a watershed into hydrological response units (HRUs) based on unique land cover, soil type, and slope. HRUs are a set of...
Identifying Priority Subwatersheds Using Distributed Modeling Approach
Pai, N., D. Saraswat, and M. Daniel. 2011. Identifying Priority Subwatersheds Using Distributed Modeling Approach. Trans. ASABE 54(6): 2181-2196.
Abstract: This article describes a modeling approach for prioritizing 12‐digit hydrologic unit code subwatersheds in the Illinois River Drainage Area in Arkansas (IRDAA) watershed utilizing the soil and water assessment tool (SWAT) model...
SWAT2009_LUC: A Tool to Activate Land Use Change Module in SWAT 2009
Pai, N. and D. Saraswat. 2011. SWAT2009_LUC: A Tool to Activate Land Use Change Module in SWAT 2009. Trans. ASABE. 54(5): 1649-1658. (2012 ASABE Superior Paper award winning paper).
Abstract: In watersheds where land use and land cover changes take place over the modeling period, using a single land use geospatial dataset is not a true representation of the watershed condition. This article describes development of...
Biofuels and Water Quality: Challenges and Opportunities for Simulation Modeling
Engel, B., I. Chaubey, M. Thomas, D. Saraswat, P. Murphy, and B. Bhaduri. 2010. Biofuels and Water Quality: Challenges and Opportunities for Simulation Modeling. Future Science Group: Biofuels. 1(3): 463-477.
Abstract: Quantification of the various impacts of biofuel feedstock production on hydrology and water quality is complex. Mathematical models can be used to efficiently evaluate various ‘what if’ scenarios related to...
Application of GPS and Near-Surface Geophysical Methods to Evaluate Agricultural Test Plot Differences
Allred, B., B. Clevenger, and D. Saraswat. 2009. Application of GPS and Near-Surface Geophysical Methods to Evaluate Agricultural Test Plot Differences. FastTimes. 14(3): 15-24.
Abstract: A field research facility with two pairs of replicated agricultural test plots (four total) was established at a location in northwest Ohio during 2005 for the purpose of studying water table...
Comparison of electromagnetic induction, capacitively-coupled resistivity, and galvanic contact resistivity methods for soil electrical conductivity measurement
Allred, B. J., R. Ehsani, and D. Saraswat. 2006. Comparison of electromagnetic induction, capacitively-coupled resistivity, and galvanic contact resistivity methods for soil electrical conductivity measurement. Applied Engineering in Agriculture. 22(2): 215-230.
Abstract: In situ measurement of apparent soil electrical conductivity (ECa) is an important precision agriculture tool useful for determining spatial changes in soil properties. Three near-surface geophysical methods are available for rapid...
The Impact of temperature and shallow hydrologic conditions on the magnitude and spatial pattern consistency of electromagnetic induction measured soil electrical conductivity
Allred, B. J., R. Ehsani, and D. Saraswat. 2005. The Impact of temperature and shallow hydrologic conditions on the magnitude and spatial pattern consistency of electromagnetic induction measured soil electrical conductivity. Trans. ASAE, 48(6): 2123-2135.
Abstract: In situ measurement of apparent soil electrical conductivity (ECa) is an important precision agriculture tool for determining spatial changes in the soil properties affecting soil fertility. However, dynamic temperature and shallow...