Location: Genetics and Sustainable Agriculture Research
Project Number: 6064-21600-001-016-A
Project Type: Cooperative Agreement
Start Date: Sep 20, 2025
End Date: Sep 19, 2027
Objective:
1. Develop dynamic, robust, and resilient cropping systems that integrate
conservation and science-based solutions to improve short- and long-term crop
production systems increase input efficiencies, and provide adaptability for
unpredictable weather and supply chain stocks.
2. Develop, expand, and deploy high throughput data acquisition and analytics
systems and platforms for multi-faceted data streams to improve the sustainability and relevancy of agricultural production systems and ecosystem services with an emphasis on soil health, production inputs, soil moisture, and soil gas.
3. Advance engineering and computational technologies for cropping system best
management practices and ecosystem services through innovations in precision
agriculture, digital transformations, advanced hardware and software technologies, autonomous systems, computer vision, and artificial intelligence.
Approach:
Proper termination method optimizes cover crop benefits in agroecosystems.
Hypothesis: Cover crop termination methods will reduce evaporative soil moisture
loss, and enhance residue decomposition rate, increase available soil N, and improve crop performance. Screen crops for efficient root systems. Development of cuttingedge sensing/imaging technologies and plant science solutions to mitigate environmental challenges. Hypothesis: Crop performance and yield potential will increase by growing stress-tolerant genotypes under extreme weather conditions. Growing deeprooted cover crop(s) followed by row crop(s) can add more atmospheric CO2 to the soil and make cash crops more capable of foraging nutrients and water under unfavorable environmental conditions. Identify traits associated with higher yield for resilient cropping system. Detection of stress symptoms has been a significant bottleneck in sustainable agriculture. Advancements in sensing and imaging now enable screening diverse cultivars for stress resilience and identifying traits associated with higher yield under new management systems. The precision phenotyping methods will be streamlined to identify varieties with high-yield and nutrient-rich seeds (nutrient and quality) in addition to physiology, canopy cover,
leaf area index, crop growth, yield, seed quality, soil and leaf water content, and tissue nutrients data. Linking soil gas, UAS, and soil biology (fungi and bacteria).
Goal: Soil biological and environmental health indicators will be identified through field and greenhouse-based experiments whereby natural and treatment “stress” will be quantified and qualified from experiments in Objective 1 to ascertain novel soil health indicators. Early diagnosis of nutrient deficiencies in row crops using UAS hyperspectral imaging. Goal: Use UAS hyperspectral imaging to accurately estimate crop canopy nutrient contents. Developing a small UAV and UGV system and associated model to monitor soil gas fluxes.
Goal: Develop and deploy an UAV and UGV system with sensors to collect soil moisture fluxes and meteorological data from small plot experiments.
Large-scale soil organic matter sensing and monitoring using a collaborative UAV - UGV
system. Goal: We propose to use an unmanned ground vehicle (UGV), a Clearpath Husky robot, equipped with a visible and near-infrared (Vis-NIR) spectroscopy sensor to carry out soil organic matter measurements. Determine the essential data resolution needed to develop effective models to quantitatively estimate crop resiliency at the genotype, environment, management, and its interactions by utilizing statistical analytics, machine learning and artificial intelligence methods.
Goal: We posit that exploring the RF spectrum for remote sensing from drones is unprecedented because no existing small UAS instrument is capable of remote sensing subfield scale soil moisture.