Digital and Precision Agriculture Lab
Where AI Meets the Field
AI & Digital Intelligence at the Core of Our Research
ABOUT US
Digital and Precision Agriculture Lab (D-PAL) is an AI-first research group dedicated to advancing innovative, data-driven solutions for sustainable and efficient agricultural practices. We integrate artificial intelligence, machine learning, remote sensing, GIS, and IoT to build decision support tools and precision strategies that optimize crop production systems. The research emphasizes site-specific management strategies that enhance productivity while minimizing environmental impacts. By bridging the gap between field-level challenges and technological innovations, the lab contributes to the development of smart farming systems tailored to diverse agro-ecological and hydrological conditions. The lab integrates a comprehensive field-lab-modeling framework, enabling a holistic understanding of soil-water-plant dynamics under variable climatic and management conditions. By combining field experiments, laboratory analysis, and process-based modeling, the lab supports the development of precision strategies that address real-world challenges in water and nutrient management, contributing to resilient and climate-smart agriculture.
Dr. Kumar is an expert in developing AI- and data-driven, water-smart irrigation solutions for site-specific precision water management in changing weather regimes. He has pioneered methods that use machine learning on field and remote-sensing data, together with system-based modeling, to estimate water stress and optimize irrigation decisions during critical crop growth stages. Additionally, Dr. Kumar has devised a novel method for optimizing management zones based field capacity to determine accurate irrigation thresholds and amounts, ensuring the upper water holding capacity of the soils. This approach underscores the necessity of variable rate irrigation as a resilient solution, addressing the challenges of water scarcity during growing seasons. Please reach out at hemendra@umd.edu or pal@umd.edu to discuss the ideas.
Kumar, H.
Precision Agriculture Specialist, College of Agriculture and Natural Resources
University of Maryland, College Park
Mission
To deliver AI-powered, science-based solutions that improve productivity, reduce environmental impacts, and support long-term agricultural resilience through site-specific strategies. We integrate machine learning, remote sensing, GIS, and IoT within a robust field–lab–modeling framework to empower producers, researchers, and decision-makers with precision tools for resource efficiency and environmental sustainability.
Vision
To set the benchmark for AI-driven precision agriculture by designing adaptable, high-impact solutions for real-world crop production challenges. The lab aims to lead in smart, scalable farming systems that combine rigorous field research with advanced analytics and ML. We translate field–lab–modeling insights into tools and strategies for resilient, efficient, and climate-adaptive agriculture.
Events Hosted by D-PAL
Lab to Host 2026 Precision Agriculture Technology Conference
February 26, 2026 · 9:00 AM – 4:30 PM
Crowne Plaza Annapolis
173 Jennifer Rd, Annapolis
Awarded Research Grants
Precision Irrigation Management for Soybean in Maryland
UAVs and AI for Crop Management and Environmental Sustainability
Drone-Based Spraying for Watermelon in the Mid-Atlantic
Precision Horticulture with UAVs for Ornamental Nurseries
Soil and Water for Irrigation Management in Maryland
Nutrient Load Reductions in Agricultural Drainage
Thematic Research Areas
AI, Digital Agriculture & Smart Technologies
- AI-Driven Agricultural Intelligence and Big Data - Predictive modeling, precision decision support, and analysis of large-scale agricultural datasets using ML and advanced data science.
- Digital Agriculture and Smart Farming Technologies - Integrated digital tools for decision-making, farm management, and automation.
- Remote Sensing, IoT, and GIS in Crop Production - Spatial and sensor-based monitoring of crop health, soil moisture, and field variability to feed AI and decision models.
Remote Sensing and Proximal Sensing for Agroecosystems Management
Use of satellite, aerial (UAV/drone), and ground-based sensors to monitor crops, soil, and water at multiple scales. Remote sensing captures spatial and temporal variability from a distance; proximal sensing provides high-resolution, in-field measurements. Together they support precision mapping of plant health, water stress, nutrient status, and yield potential for data-driven management.
Agricultural Water Management
- Climate, water, and field-smart irrigation - Adaptive strategies that respond to climate variability, soil heterogeneity, and crop water demand for resilient and efficient water use.
- Controlled drainage and automated water table management - Innovations that optimize water retention, reduce nutrient loss, and support both drought and flood resilience.
- System-level irrigation optimization - Data-driven scheduling and technology integration to maximize water-use efficiency and crop performance.
- Variable rate irrigation (VRI) - Spatially targeted water delivery using real-time field data and precision equipment for site-specific management.
Modeling and Soil Science
- System-based modeling - Integration of physical and data-driven models to simulate soil-water-plant dynamics and support precision irrigation and resource management decisions.
- Developing and Optimizing Soil Hydraulic Properties (SHPs) to Improve Crop and Hydrologic Modeling - Advancing methods to accurately characterize and parameterize soil hydraulic properties, enabling more reliable simulations of soil-water dynamics and improving the predictive accuracy of the models.
- Crop Modeling Integrated with Lab and Field Measurements - Development and calibration of process-based models using empirical data to simulate crop growth and soil-water interactions.
Our Latest Edited Books
Precision Technologies for Digital Agriculture: Harnessing IoT, Big Data, Crop Modeling, and AI for Agricultural Production
Edited by: Sushil Kumar Himanshu, Hemendra Kumar, Pankaj Kumar Gupta
Publisher: Elsevier, Amsterdam, Netherlands
ISBN: 9780443405136 (In Press)