Dharmendra Saraswat
Agricultural and Biological Sciences · Purdue University West Lafayette
Publications
66
Citations
1,919
Est. group size
~5
Recurring co-author estimate
Active years
24
Publishing since 2003
Dharmendra Saraswat's work applies computer vision and deep learning to agricultural problems, especially detecting and identifying weeds and plant diseases (like corn tar spot) from images taken in fields or under controlled conditions. The research also extends to broader agricultural and water-resource questions, including climate change impacts on irrigation, evapotranspiration, and drought prediction using machine learning. Overall, this is applied engineering research aimed at building practical tools such as smart weed identification systems, disease severity estimators, and decision-support datasets for farmers and researchers.
Publication output grew notably from 2017 through a peak around 2022, has remained fairly active in recent years, with a slight dip in 2025-2026 compared to the 2021-2024 period.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Deep Learning-Based Computer Vision Methods for Smart Weed Identification
2026
- Comprehensive Review of Artificial Intelligence and Edge Computing for Precision Weed Control
Artificial Intelligence and Applications · 2026
- Assessing TensorRT Optimization for Real-Time Object Detection in Agriculture
2025
- Implications of <scp>CMIP6</scp> Models‐Based Climate Biases and Runoff Sensitivity on Runoff Projection Uncertainties Over Central India
International Journal of Climatology · 2024
- Assessing Regional-Scale Heterogeneity in Blue–Green Water Availability under the 1.5°C Global Warming Scenario
Journal of Applied Meteorology and Climatology · 2024
- The utility and applicability of vegetation index-based models for the spatial disaggregation of evapotranspiration
Irrigation Science · 2024
- Economic Assessment of Transforming Rainfed to Irrigated Agriculture in a Drought-Prone Region of Central India&#160;
2024
- Analyzing trends for agricultural decision support system using twitter data
arXiv (Cornell University) · 2024
- Drones, Robots, and Tractors – Creating an Agricultural Mechatronics Curriculum for use in High School Classrooms
2024
- Toward Generalization of Deep Learning-Based Plant Disease Identification Under Controlled and Field Conditions
IEEE Access · 2023
- Implications of 1.50C global warming for agricultural productivity over a global rice exporting region in Central India
2023
- Datacentricdl: Impact of Training Data on Deep Learning-Based Weeds and Disease Identification for Precision Agricultural Applications
SSRN Electronic Journal · 2023
- Pilot Course in Data Visualization with a Multidisciplinary Approach: Technology+Agricultural Engineering
2023
- A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools
Smart Agricultural Technology · 2022
- A two-stage deep-learning based segmentation model for crop disease quantification based on corn field imagery
Smart Agricultural Technology · 2022
- arXiv (Cornell University)×4
- Smart Agricultural Technology×3
- Journal of the ASABE×3
- IEEE Access×2
- Remote Sensing×2
- Bruce Erickson
Agricultural and Biological Sciences · Purdue University West Lafayette
- Aaron Ault
Agricultural and Biological Sciences · Purdue University West Lafayette
- Andrew Balmos
Agricultural and Biological Sciences · Purdue University West Lafayette
- Amogh Joshi
Agricultural and Biological Sciences · Purdue University West Lafayette
- Changye Yang
Agricultural and Biological Sciences · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
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