Remote sensing for Agro-informatics
We develop monitoring systems that can provide globally precise metrics on how cropland is being used. These systems boost various applications (e.g., tree crops mapping and yield predictions) to feed increasing the world population and fight poverty for undeveloped countries.
1. NASA LCLUC: Evaluating land use change and livelihood responses to large investments for high-value agriculture: managing risks in the era of the Green Morocco Plan
This project evaluates the social and environmental consequences of large-scale agricultural investments, focused on (1) the transition from cereals to perennial crops in the drought-prone Mediterranean country of Morocco and (2) the possibility to use remotely-sensed indicators of environmental stress at the basis of responsible, adaptive relief financing.
2. TechnoServe: Mapping Cashew Plantations in Benin
Methods for mapping tree crops or plantations are limited in their spatiotemporal resolution and scalability. Spectral information (e.g. NDVI) alone is often not sufficient to distinguish perennial crops like fruits, nuts and vineyards from other types of vegetation. This project will use variants of deep neural network and very-high-resolution satellite imagery to map cashew plantations (area and counts). Multiple attention-based neural networks will be explored to predict cashew yield and farmers’ practices.
3. Prosper cashew: Map the cashew plantation to help build a thriving and sustainable West African cashew processing industry
4. PepsiCo: Develop mechanistic models and key remote sensing features for Opt-Oat: integrated research of crop modeling, remote sensing, and field zoning
Improving agroecosystem modeling for sustainable management
To maintain food security and environmental sustainability, this research involves process-based modeling and AI-driven methods to track agroecosystem greenhouse gases and biogeochemical cycles of water, C, N and P.
1. NSF SCC-IRG Track 1: Co-Producing Community – An integrated approach to building smart and connected nutrient management communities in the US Corn Belt
Enabling farmers to manage N and P with greater precision is needed to increase farmer profitability and decrease off-farm losses of nutrients, which can compromise water resources. The objective of this project is to develop science-driven recommendations on N and P management that can be tailored to different farmers’ needs, focusing on the heart of the US Corn Belt: Illinois. Through this project, we will: (i) identify major constraints on how Illinois farmers manage N and P; (ii) determine how much N and P are stocked in soils across a diversity of Illinois farms using soil sampling, soil sensors and satellite observations, and how this soil nutrient capital contributes to crop growth in order to model field-specific fertilizer needs; (iii) develop smart and connected technology solutions that enable constrained farmers to join a Nutrient Management Community.
2. NSF SitS: Spatial and Temporal Patterns of Soil N and P Cycles Quantified by a Sensor-Model Fusion Framework: Implications for Sustainable Nutrient Management
High crop productivity in the Midwestern US was achieved by artificially draining wetlands and applying millions of tons of nitrogen (N) and phosphorous (P) fertilizers. However, 40-80% of these N and P nutrient inputs are lost from soils and become pollutants in water bodies and the atmosphere. This project will integrate recent advances in nanotechnology, sensing technology, and machine learning to enable new methods for measuring and managing N and P in croplands to reduce losses to the environment. The outcomes of this project can be used directly by farmers to better manage field application of N and P fertilizers and by local/federal governments and other organizations to pinpoint pollution hotspots and develop strategies for nutrient reduction.
3. DOE ARPA-E SMARTFARM: The “System of Systems” Solutions for Commercial Field-Level Quantification of Soil Organic Carbon and Nitrous Oxide Emission for Scalable Applications (SYMFONI)
Accurate and rapid field-level quantification of carbon intensity at a regional scale is critical to facilitate adoption of new technologies to increase the bioeconomy’s feedstock productivity and reduce its carbon footprint. This project will develop a commercial solution, SYMFONI, to estimate soil organic carbon and the dynamics of nitrous oxide emissions at an individual field level. The solution can be scaled up to perform per-field estimates for an entire region. SYMFONI is a “system of systems” solution that integrates airborne-satellite remote sensing, process-based modeling, deep learning, atmospheric inversion, field-level sensing, and high-performance computing.
4. USAID SIIL Geospatial, Farming Systems, and Digital Tools Consortium: Building a New Era of Predictive Agricultural Innovation to Improve the Livelihood of Smallholder Farmers
This consortium will build upon the five domains — productivity, economics, environment, social and human condition — of the sustainable intensification framework, developing an interdisciplinary and solution-oriented geospatial framework, integrating remote sensing, farming systems modeling, and geospatial data layers to provide innovative data products to take actions toward more resilient farming systems, benefiting families and communities. Providing access to simple digital tools to researchers, extension personnel, policymakers and practitioners will allow them to make informed decisions to minimize risk and improve the resilience of people and farming systems.
5. USDA NIFA: High-resolution integrated assessments of tillage practice impacts on crop production and agroecosystem sustainability in the US Midwest – combining meta-analysis, airborne-satellite sensing, and process-based modeling
Tillage is an essential farming practice that is closely tied to production cost, crop yield, and environmental sustainability. In this project, we aim to innovatively integrate meta-analysis, airborne and satellite data, and process-based modeling to conduct high spatiotemporal assessments of tillage impacts on crop productivity and environmental sustainability in the US Midwest. The following four major Midwest states will be included as our study domain: Illinois, Indiana, Iowa, and Minnesota. In particular, we will study the tillage impacts on crop yield of corn and soybean (the two dominant crops grown in the US Midwest), and several environmental sustainability metrics: greenhouse gas emission, nitrogen leaching, changes in soil organic carbon, and soil erosion. We aim to achieve the following objectives: (1) Collect historical field data from the literature to conduct meta-analyses to quantify impacts of various tillage practices on crop yield and environmental sustainability metrics; (2) Use airborne hyperspectral imaging data, multi-sensor fused satellite data, and machine-learning algorithms to map field-scale tillage practices for the historical period (2000-2023) for the four major Midwest states, and quantify temporal trends of tillage practices at the field scale; (3) Use long-term experimental data, meta-analysis results, and satellite-based measurements to constrain and validate the Ecosys model simulation on tillage impacts for crop yield and environmental sustainability; and (4) Quantify the impacts of tillage practices on crop production and environmental sustainability using the constrained Ecosys model and satellite-based tillage maps for the major Midwest states and conduct suitability assessment of tillage practices to support policy design and farmers’ decision making.
Climate Change Adaptation and Mitigation
We develop models exploring the mechanisms of climate change impacts and then reducing flow-trapping of greenhouse gases into the atmosphere and adjusting to a expected future climate.
WinterTurf: A Holistic Approach to Understanding the Mechanisms and Mitigating the Effects of Winter Stress on Turfgrasses in Northern Climates
Winter stresses have long been a challenge for professional turfgrass managers. The long term goal of this project is to better understand turfgrass winter stresses and then identify solutions to this important specialty crop problem. Our approach will include research in plant breeding, genetics, and physiology; efforts to improve resistance to plant disease and thereby reduce pesticide applications; new technological innovations to predict winter stress damage more precisely; and modern approaches to engage and educate stakeholders. The objective that our group is involved will monitor turfgrass stands in cold climates to gain knowledge about the processes that lead to winter damage and how a changing climate will bring new challenges; then use this information to both inform research and create a web-based winter stress injury prediction application. We aim to provide stakeholders with solutions to combat the challenge of winter stresses.
Controlled Environment and Urban Agriculture
As climate change has posed a threat to traditional agriculture, this research aims to use controlled environment to produce high-quality food with optimizing resource-use efficiency. We develop a system to automate farm process, including planting, harvesting, ripeness detection, and yield prediction.
MN Robotics Institute: Robotic Manipulation Towards Fully Autonomous Indoor Farming
Controlled environment agriculture (CEA) such as hydroponics and aeroponics in the greenhouse or vertical farms represents a massive leap in progress toward true agricultural intensification, sustainability, and efficiency. These emerging forms of farming have attracted a surge of interest largely due to their huge potential in boosting productivity. In some well-managed modern greenhouse, reclamation of water, digitally controlled light, nutrients, and microclimate mean that 20 times more yield per acre land can be produced with 90% less water than traditional methods. However, the high labor cost is one of the major barriers for these CEA facilities to be economically feasible. Automation in a cost-effective way is thus the primary demand of this industry. This seed grant supports our pilot efforts on design a robot hand that is capable of planting and harvesting task and fruit ripeness detection and yield prediction.
