Source: UNIVERSITY OF CALIFORNIA, DAVIS submitted to
AI INSTITUTE: NEXT GENERATION FOOD SYSTEMS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1024262
Grant No.
2020-67021-32855
Cumulative Award Amt.
$20,189,383.00
Proposal No.
2020-09154
Multistate No.
(N/A)
Project Start Date
Sep 1, 2020
Project End Date
Aug 31, 2025
Grant Year
2024
Program Code
[A7303]- AI Institutes
Project Director
Tagkopoulos, I.
Recipient Organization
UNIVERSITY OF CALIFORNIA, DAVIS
410 MRAK HALL
DAVIS,CA 95616-8671
Performing Department
Computer Science
Non Technical Summary
Artificial Intelligence (AI) has the potential to transform US food systems by targeting its biggest challenges: improving food yield, quality, and nutrition, decreasing resource consumption, increasing safety and traceability, and eliminating food waste. In the last decade, scientists and engineers have made significant headway in developing and deploying tools and devices that deliver a massive, yet too often raw, data stream to food system stakeholders at unprecedented spatiotemporal resolution. At the same time, AI algorithms repeatedly break benchmarks in computer vision, natural language processing, and automation, while AI-optimized hardware is enabling major advances from robotics to consumer electronics. Despite big leaps in AI capacity, food systems, like other complex domain areas, present several challenges in the application and adoption of AI: (1) Food systems are highly diverse and biologically complex, (2) ground truth data is sparse, costly, and privately held, and (3) human decisions and preferences are intricately linked to every stage of food system supply chains.The mission of the AI Institute for Next Generation Food Systems (AIFS) is to develop AI technologies for a sustainable food system and to nurture next generation talent to produce and distribute nutritious food with fewer resources. In the coming decades, AIFS aims to transform US food systems by innovating AI technology that will generate actionable information for diverse stakeholders in food system supply chains, grounded in a robust ethical and socioeconomic framework. Toward this goal and addressing the above challenges, AIFS will develop generalizable, data-efficient, and trustworthy AI solutions to enable (1) Molecular breeders to discover and/or design the next generation of high yielding, high-quality, consumer-focused foods, (2) Agricultural producers to maximize food quantity and quality, while minimizing resource consumption and waste, (3) Food processors and distributors to deliver highly traceable and safe food, while minimizing resource consumption and waste, and (4) Consumers to rapidly and precisely assess the nutrition of a meal, quantify the food's molecular composition, and predict the impact on their health. AIFS will build these solutions using a knowledge-driven and human-in-the-loop learning paradigm designed to handle food system diversity and biological complexity, efficiently capture and utilize food system data, and garner user trust via explainability, safety, privacy, and fairness. Today, when AI is employed by food system researchers, engineers, and industry leaders, it is almost exclusively as a technological byproduct of other industries. By creating food system-specific AI solutions, AIFS will accelerate AI's capacity to positively transform US food systems and impact stakeholders across the supply chain.Given food's fundamental role in human health and well-being, along with its far-reaching impacts on the national economy and environment, creating a multidisciplinary institute focused on AI innovation in agriculture and food systems is critical. AIFS will bring together researchers from six institutions (i.e. University of California, Davis [UCD]; University of California, Berkeley [UCB]; Cornell; and University of Illinois, Urbana-Champaign [UIUC], USDA-ARS, University of California, Agriculture and Natural Resources [UC ANR]) with a proven record of excellence in AI and food system science, engineering, outreach, and education. AIFS will serve as a national nexus point for collaborative efforts spanning higher education institutions, federal agencies, industry, and nonprofits/foundations. In this brokerage capacity, AIFS will accelerate the translation of AI innovations into the food system and nurture the next generation of talent to enable a more resilient and productive society.AIFS will have six research clusters, with two foundational research areas (Use-Inspired and Foundational AI, and Socioeconomics and Ethics) and four application research areas (ARA) (Molecular Breeding, Agricultural Production, Food Processing and Distribution, and Nutrition) in addition to programs in Education, Outreach, and Workforce Development (EOWD), Broadening Participation and, Collaborations and Knowledge Transfer. Application research areas will span the entire food system. The Use-Inspired and Foundational AI cluster will connect the six research clusters and develop AI tools through close communication, and feedback cycles. Social, economic and ethical considerations will be integrated into the application of AI in all four applied research areas. AIFS will actively engage academic, stakeholder and public audiences through education, outreach and broadening participation activities (e.g. roundtables, exhibits, panel discussions, etc.) led by graduate students and postdoctoral fellows supported by this project as part of the UCD Institute for Food and Agricultural Literacy (IFAL). IFAL and AIFS will collaborate with community colleges in both the research and application of technologies in industry through internships, fellowships, competitions and conferences. AIFS Workshops led by UC ANR, a statewide UC network of over 1,500 academics and staff with the mission to transfer science and technology to the people of California will serve to inform and train industry professionals on the application of AI technologies. Finally, AIFS will leverage UC ANR's global innovation food and agriculture innovation network, the Verde Innovation Network for Entrepreneurship (the VINE) to accelerate commercialization using an environmentally safe and ethically sound approach, through partnership with existing companies and promoting entrepreneurship.AIFS aims to develop food system-centric AI solutions for transforming productivity, sustainability, and safety of food systems as well as enhancing consumer health and wellness. These AI solutions will innovate algorithms and computational resources to model both diversity and biological complexity of food systems, address key knowledge gaps in ground truth data, and create explainable and trustworthy predictions to engage humans in-the-loop. These innovations are significantly and intellectually distinct from the current scenario where AI approaches in food systems are exclusively technological by-products of other industries. By investigating and creating food system-specific AI technologies, AIFS will accelerate AI's capacity to positively transform US food systems and impact stakeholders across the supply chain. AIFS will bring together researchers from six institutions with a proven record of excellence in AI, food system sciences, and engineering. The proposed research plan will investigate original and transformative concepts at the intersection of foundational and application research areas that span the entire food system. Within each application research area, AIFS addresses three critical challenges, a specific set of food system stakeholders confronting these challenges, foundational AI research solutions, and a corresponding socioeconomic and ethical framework. Critically, AIFS institutions represent leaders in AI innovation and agriculture and food systems research with significant resources including state-of-the-art compute, molecular sequencing, analytical, greenhouse, crop production, and engineering facilities as well as stakeholder engagement to enable success and transformative impact on society.
Animal Health Component
50%
Research Effort Categories
Basic
30%
Applied
50%
Developmental
20%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
7010001101015%
2010001104015%
5010001202015%
6010001301015%
5020001302015%
8030001302015%
9010001202010%
Knowledge Area
601 - Economics of Agricultural Production and Farm Management; 803 - Sociological and Technological Change Affecting Individuals, Families, and Communities; 501 - New and Improved Food Processing Technologies; 901 - Program and Project Design, and Statistics; 201 - Plant Genome, Genetics, and Genetic Mechanisms; 701 - Nutrient Composition of Food; 502 - New and Improved Food Products;

Subject Of Investigation
0001 - Administration;

Field Of Science
1040 - Molecular biology; 2020 - Engineering; 3020 - Education; 1010 - Nutrition and metabolism; 3010 - Economics;
Goals / Objectives
The mission of the AI Institute for Next Generation Food Systems (AIFS) is to develop AI technologies for a sustainable food system and to nurture next generation talent to produce and distribute nutritious food with fewer resources.The overall vision of AIFS is to address challenges in both foundational and use-inspired AI research, train the future AI workforce, and address some of society's grand challenges across the food system. The long-term research goal is to transform US food systems by innovating AI technology that generates actionable information for diverse stakeholders along food system supply chains and is grounded in a robust ethical and socioeconomic framework.The AIFS institute has 2 foundational and 4 application research areas (6 clusters total), each with its own goals. These are:Foundational Research Area 1: Use-inspired and Foundational AIResearch Objectives: AI and data-driven computational methods are the underlying fabric that connect the application research areas of AIFS. The objectives of this research area, in a logical progression of effort, are as follows: (a) identify key common challenges that underlie the entire pipeline of the food system; (b) establish theoretical frameworks within which these challenges can be systematically addressed; (c) develop use-inspired methods and algorithms that can be refined and extended to take into account the specifications and domain knowledge of each of the four application areas; (d) establish foundational principles and understandings that are salient in an AI-enabled agricultural science and generalizable to other scientific fields. AIFS will place on the balance between foundational research and agricultural application-specific solutions through a principled and systematic investigation that tackles critical challenges inherent in the food system.Foundational Research Area 2: Socioeconomics and Ethics We propose to develop a model of socially trustworthy AI for agricultural applications. Our process for developing this framework consists in answering a series of straightforward questions expressed in lay terms:1) What is it that AI developers and producers are asking people to trust them with, to accomplish what purpose?2) What are the accountability, safety, and precautionary methods and practices that are in place to assure that AI developers/producers can be trusted to do the things they claim to want to do with the data they are soliciting?3) How effective are those methods and practices, since an endeavor is only as trustworthy as its weakest accountability measure and practice?4) How vigilant are AI developers and producers in attempting to identify new concerns and issues that can arise from their AI applications and who is responsible for guarding against them? To whom are they accountable and what are the mechanisms used to hold them accountable?5) How will we train grad students and postdocs in ethics of publishing and transparency?Application Research Area 1: Molecular BreedingBuilding on the developments in the AI cluster we aim to develop AI methods that are Explainable to breeders, that are Context-aware to adapt to consumer preferences, and that leverage Data Integration to take full advantage of the wave of automated high-throughput phenotyping technologies currently being applied to diverse breeding programs.Application Research Area 2: Agricultural ProductionThe Agricultural Production thrust will develop AI tools that enable agricultural producers to sustainably manage the diversity of horticultural crops - maximizing food yield and quality, while minimizing resource consumption and waste. Specifically, we aim to address the following three challenges associated with agricultural production: 1) Highly variable production conditions. Crop monitoring, forecasting, and mechanization is highly site-specific due to variability in crop traits, pathogen pressures, environmental conditions, and management strategies making technological generalization very challenging. 2) Low to no internet connectivity, limited memory, and low power supply. Agricultural production technology faces unique constraints as it often occurs in remote areas with low to no internet connectivity, limited memory, and limited power supply. 3) Technology adoption requires producer confidence. Buy-in to decision-support and automation technologies requires agricultural producer confidence in these systems' capacity to produce valuable information and actions.Application Research Area 3: Food Processing and DistributionThe Food Processing and Distribution cluster aims to develop generalizable AI frameworks to address the critical gaps in the areas of food safety, food loss and spoilage and sustainability of food processing.Application Research Area 4: NutritionThe goal of the nutrition cluster is to answer the following question: How can advancements in AI enable rapid and precise assessment of what people are eating, quantify that food's molecular composition, and predict the impact on their health? To make progress towards this grand challenge, we propose to leverage AI for improved dietary assessment, the prediction of a particular class of molecules in food (glycans), and the prediction of the colonic response.In addition, the EDUCATION, OUTREACH AND WORKFORCE DEVELOPMENT (EOWD) cluster will be guided by a driving philosophy of co-creation: industry-university collaboration, focus on research translation and entrepreneurship, science-based decision making for real industry challenges, and communicating the value and promise of technology to a variety of audiences. This project will drive critical advances in AI models, tools, and use-inspired solutions for molecular breeding, precision agriculture, and sustainable food systems that will lead to the production of higher quality, more sustainable and more profitable crops, food and fiber. As they are developed, these technologies should be rapidly introduced into the market and workforce through innovative approaches to education, outreach, and workforce development, which will incorporate four critical goals: a) improve access, awareness, and interest amongst K-12 and community college undergraduate students, including non-traditional and underrepresented student populations, towards completing degrees and training focused on AI for the food system, b) increase the number of highly-competent AI-trained and skilled new workforce entrants across food and agriculture sectors and disciplines, c) implement effective industry and government partnerships to accelerate market adoption of AI food and agriculture technologies, and d) incorporate AI into our successful and comprehensive outreach program (IFAL) that trains students and postdocs to more effectively engage with the public. As part of this grant our students will communicate the value of new and complex AI technologies in ways that promote public support and integrate scientifically sound information into the public discourse.
Project Methods
AIFS aims to develop food system-centric AI solutions for transforming productivity, sustainability, and safety of food systems as well as enhancing consumer health and wellness. These AI solutions will innovate algorithms and computational resources to model both diversity and biological complexity of food systems, address key knowledge gaps in ground truth data, and create explainable and trustworthy predictions to engage humans in-the-loop. These innovations are significantly and intellectually distinct from the current scenario where AI approaches in food systems are exclusively technological by-products of other industries. By investigating and creating food system-specific AI technologies, AIFS will accelerate AI's capacity to positively transform US food systems and impact stakeholders across the supply chain. AIFS will bring together researchers from six institutions with a proven record of excellence in AI, food system sciences, and engineering. The proposed research plan will investigate original and transformative concepts at the intersection of foundational and applied research areas that span the entire food system. Within each application research area, AIFS addresses three critical challenges, a specific set of food system stakeholders confronting these challenges, foundational AI research solutions, and a corresponding socioeconomic and ethical framework. Critically, AIFS institutions represent leaders in AI innovation and agriculture and food systems research with significant resources including state-of-the-art compute, molecular sequencing, analytical, greenhouse, crop production, and engineering facilities as well as stakeholder engagement to enable success and transformative impact on society.To build generalizable, trustworthy, and data-efficient AI solutions, our overarching approach rests on a knowledge-driven and human-in-the-loop learning paradigm. Specifically, human expert knowledge is leveraged to tame the complex learning space and to mitigate the stringency of data. Human-in-the-loop learning allows active and real-time interactions between human and machine, which is indispensable for building trust, for obtaining labels that are difficult to define, often subjective (e.g. sensory and flavor attributes), and for constructing reward/loss functions. One key issue in human-in-the-loop learning is that human involvement can be costly, time consuming, and subject to bias. It is thus crucial that such human-machine interactions be optimized and regulated in terms of when and what. This issue will be addressed through a close collaboration with the foundational research area on socioeconomics and ethics

Progress 09/01/22 to 08/31/23

Outputs
Target Audience:For our third year of operations, we expanded our target audience to include additional industrial and academic partners. More specifically, our target audience was: 1. Scientific community. In its third year, AIFS supported 6.3 PIs, 59 trainees (3 undergraduate, 52 graduate, and 3 postgraduate or other trainees), 54of them in STEM areas. This group has published more than 50 publications in peer reviewed journals and conferences. There have been more than 35public talks through various outlets and organizations, including the National Academies and the Ag Robotics Conference (FIRA) among others 2. Industry partners. Through our business development activities, we have engaged more than 50 companies in the area of AI and Food Systems. The target audience were principal scientists and engineers in food systems and data science, business development associates, leadership (CEO,CTO,CDO, etc.), among others. 3. Other stakeholders. These include (a) media sources (we provide content from our experts), (b) governmental and NGO groups, (c) broader community and practitioners, who want to use or learn about AI Ag and Tech, (d) entrepreneurs and investors, (e) service providers to farmers and the broader agricultural supply chain. In addition, our public engagement and education efforts also targeted: • Grad students/postdocs at AIFS organizations via AIFS education modules • AIFS researchers in general via AIFS internal seminars • Undergrads via the Career Exploration Fellowship Program and machine learning Bootcamp called AIBridge. • General public, subcategories including academics and students outside AIFS, individuals in the industry, farmers/agricultural workers • Employees in general at participating universities and other institutes who see news feeds regarding our engagement work • Viewers of newsletters and social media posts. • Public attending conferences that we co-sponsor or provide moderators or speakers for ? Changes/Problems:There have been no major changes or problems in approach. What opportunities for training and professional development has the project provided?AIFS has developed a thorough drone curriculum for use by high school teachers in laying the foundation for digital ag related workforce development at the high school level.The goal of AIFS' Career Exploration Fellowship Program is to prepare undergraduate students from diverse backgrounds for careers at the intersection of food, agriculture, and technology. The program pairs college students with companies and university labs to work on exciting projects that are addressing critical challenges in food and agriculture using technology. Students spend 8 weeks working full time at their placements during the summer, and present on their experiences at the end of the program. Throughout their fellowships, students participate in cohort activities designed to expand their professional skills and knowledge of career paths. This program is open to undergraduates and community college students at schools in the United States, and applications open in February. Our undergraduate career exploration fellowship cohort during summer 2023 included 13 students. How have the results been disseminated to communities of interest?AIFS uses a multi-prong approach tarting various communities of interest, including a Youtube channel (https://www.youtube.com/channel/UCyvVBZ6Qx34ElPB0UmoEF2A), LinkedIn (https://www.linkedin.com/company/aifoodsystems/), X (formerly known as Twitter and transitioning the Threads), and its own website, aifs.ucdavis.edu. AIFS also maintains a mailing list using the professional system MailChimp, to reach an wide audience of hundreds of interested parties ranging from business to researcher, to government, to other stakeholders. What do you plan to do during the next reporting period to accomplish the goals?AIFS researchers will collaborate across the three thematic focus areas to produce impactful reasearch across ourprojects addressing these themes. AIFS leadership will provide opportunities for connecting this research to novel uses in the food system+ AI space, and will convene regular speakers, speaking opportunities, presentations, and reviews to allow for the ability to gauge impact and progress along the way. To achieve its Education, Outreach, and Workforce Development goals, AIFS will continue to expand existing programs and launch new programs in Year 4. The Career Exploration Fellowship Program will return in summer 2024for a new cohort of undergraduate students. The Teacher Fellowship Program will continue refining the curriculum modules for public release next summer. AIFS plans to expand educational module offerings. We will teach AIBridge again and will also host an Apps for Food and Ag hackathon. We will continue hosting technical trainings conferences, and our speaker series. Additionally, we plan to continue growing our social media and web presence.

Impacts
What was accomplished under these goals? AIFS researchers havecollaborated across the three thematic focus areas to produce impactful reasearch across 13projects addressing these themes. AIFS leadership have provided opportunities for connecting this research to novel uses in the food system+ AI space, and has convened regular speakers, speaking opportunities, presentations, and reviews to allow for the ability to gauge impact and progress along the way. To achieve its Education, Outreach, and Workforce Development goals, AIFS has continued to expand existing programs and launch new programs in Year 3. The Career Exploration Fellowship Program returned in summer 2023 witha new cohort of undergraduate students. The Teacher Fellowship Program continued refining the curriculum modules for public release. AIFS has expanded the type and comain of educational module offerings to include video modality, and a fresh thorough treatment of drones for use by teachers in high schools. We have expanded AIBridge via train-the-trainer events during actual AIBridge events at new locations including Cornell University and University of Illinois, both subaward sites, as well as a public library. We also hosted and Apps for Food and Ag hackathon and an Ag Robotics challenge. Additionally, we have continued to grow our social media and web presence, doubling our LinkedIn followers to approximately 2000.

Publications

  • Type: Journal Articles Status: Published Year Published: 2023 Citation: C. Huang, S. Ke and X. Liu, "Duopoly Business Competition in Cross-Silo Federated Learning," in IEEE Transactions on Network Science and Engineering, doi: 10.1109/TNSE.2023.3297880.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: S. Ke, C. Huang and X. Liu, "On the Impact of Label Noise in Federated Learning," in IEEE WiOpt, 2023.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: S. Ke, C. Huang and X. Liu, "Quantifying the Impact of Label Noise on Federated Learning," in AAAI Workshop on Representation Learning for Responsible Human-Centric AI, 2023.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Yixing Lu , Rewa Rai, Nitin Nitin, Image-based assessment and machine learning-enabled prediction of printability of polysaccharides-based food ink for 3D printing, Food Research Intl, 2023.
  • Type: Journal Articles Status: Under Review Year Published: 2023 Citation: C. Huang, M. Tang, Q. Ma, J. Huang and X. Liu, ``Promoting collaborations in cross-silo federated learning, submitted to IEEE Communications Magazine, under review.
  • Type: Journal Articles Status: Other Year Published: 2023 Citation: C. Huang, J. Dachille, and X. Liu, ``When federated learning meets oligopoly competition: stability and model differentiation, under preparation for submission to IEEE Transactions on Network Science and Engineering.
  • Type: Other Status: Other Year Published: 2023 Citation: Raja, P., Joshi, A., Guevara, D., Bailey, B., and Earles, J.M. (In preparation) All You Need is One: A Case Study on Vision Foundation Models for Agriculture.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2023 Citation: Guevara, D. Joshi, A., Forrestel, E., Bailey, B., and Earles, J.M. (Accepted). Enhancing Walnut Leaf-Branch Semantic Segmentation in Point Clouds: The Role of Synthetic Data. 2023 International Conference on Intelligent Robotics and Systems.
  • Type: Journal Articles Status: Other Year Published: 2023 Citation: C. Huang, J. Dachille, and X. Liu, ``Enabling light-weight split federated learning with heterogeneous data, under preparation for submission to IEEE Transactions on Mobile Computing.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Poster presentation at ACSIC 2023
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Poster presented during USDA A1364 2023 project directors meeting
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Joshi, A., Guevara, D., and Earles, J.M. (2023). Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models. Plant Phenomics. https://doi.org/10.34133/plantphenomics.0084
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Jafarbiglu, H., & Pourreza, A. (2022). A comprehensive review of remote sensing platforms, sensors, and applications in nut crops. Computers and Electronics in Agriculture, 197, 106844.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Jafarbiglu, H., & Pourreza, A. (2023). Impact of sun-view geometry on canopy spectral reflectance variability. ISPRS Journal of Photogrammetry and Remote Sensing, 196, 270-286.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Chakraborty, M., Pourreza, A., Zhang, X., Jafarbiglu, H., Shackel, K. A., & DeJong, T. (2023). Early almond yield forecasting by bloom mapping using aerial imagery and deep learning. Computers and Electronics in Agriculture, 212, 108063.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Oswald, D., Pourreza, A., Chakraborty, M., Khalsa, S.D.S., Brown, P.H. (2023) Comparative Study of Radiative Transfer and Empirical Modeling for Foliar Nitrogen Estimation in Californian Almonds Using Hyperspectral Remote Sensing (Under Review Computers and Electronics in Agriculture)
  • Type: Journal Articles Status: Accepted Year Published: 2023 Citation: Ashkan Zehfroosh, Stavros G. Vougioukas and Zhaodan Kong, Efficient Re-synthesis of Control Barrier Function via Safe Exploration, IEEE Conference on Decision and Control (CDC), Singapore, 2023.
  • Type: Journal Articles Status: Accepted Year Published: 2023 Citation: Peng Wei, Prabhash Ragbir, Ashkan Zehfroosh, Stavros G. Vougioukas and Zhaodan Kong, Vision-based Navigation of Unmanned Aerial Vehicles in Complex Environments: An Imitation Learning Approach, Journal of Field Robotics.
  • Type: Conference Papers and Presentations Status: Under Review Year Published: 2023 Citation: Guevara, D., Joshi, A., Raja, P., Forrestel, E., Bailey, B., and Earles, J.M. (in review). An open-source simulation toolbox for annotation of images and point clouds in agricultural scenarios. 18th International Symposium on Visual Computing (ISVC23).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Digital Ag  BASF (22-01-19)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Digital Ag  Microbium conference NC (22-02-24)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Dean Advisory Board (22-05-4)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Digital Ag Gates Foundation (22-07-28)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: MARS presentation - N monitorin by RS -23-3-30
  • Type: Journal Articles Status: Under Review Year Published: 2022 Citation: Kisekka, I., S. R. Peddinti, W. P. Kustas, A. J. McElrone, N. Bambach-Ortiz, L. McKee, W. Bastiaanssen. (2022). Submitted to Irrigation Science under Review (will credit AIFS and NIFA)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Integration of sensing and crop modeling into breeding pipelines, for improvement of nutritional quality and abiotic stress tolerance. Keynote, North American Plant Phenotyping Network conference, St. Louis, MO.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Building genomic predictions in maize using hundreds of trials; Corteva New Frontiers in Breeding Technologies conference, Johnston, IA.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Breeding spinach for nitrogen use efficiency; California Seed Association meeting, Dana Point, CA.
  • Type: Journal Articles Status: Under Review Year Published: 2023 Citation: J. Zha, X. Wu, R. Dimick, and M. W. Mueller: Design and control of a collision-resilient aerial vehicle with an icosahedron tensegrity structure, IEEE/ASME Transactions on Mechatronics (TMECH) (under review)
  • Type: Journal Articles Status: Other Year Published: 2023 Citation: J. Zha, T. Yang and Mark W. Mueller: Agri-fly: simulator for uncrewed aerial vehicle flight in agricultural environments (in preparation)
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2023 Citation: OBrien, A., & Abergel, R. (2023). Fitting Monte Carlo simulation results with an empirical model of megavoltage x-ray beams for rapid depth dose calculations in water. Physics in Medicine and Biology. (pending)
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Zheng, T., Bujarbaruah, M., St�rz, Y. R., & Borrelli, F. (2023). Safe Human-Robot Collaborative Transportation via Trust-Driven Role Adaptation. 2023 American Control Conference (ACC). IEEE.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Zheng, T., Bujarbaruah, M., & Borrelli, F. (2023). Data-Driven Optimization for Deposition with Degradable Tools. 22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Y. Kim, A. El-Moghazy, and N. Nitin. 2023. Photodynamic inactivation of plant pathogenic fungus on fresh produce using food-grade plant-derived antimicrobials and sunlight. ACS Fall 2023. San Francisco, CA, USA.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: OBrien, A. (2022, October 15-19) An AI-Enhanced Monte Carlo Radiation Transport Approach to Dose Distributions in Food Irradiation [Poster Presentation]. Radiation Research Society Annual Meeting, Waikoloa Village, Hawaii, United States.
  • Type: Other Status: Published Year Published: 2023 Citation: J. Zha, PhD Seminar, Expanding the Operational Environments of UAVs: Design, Control, and Motion Planning for a Tensegrity Aerial Vehicle and an Uncrewed Underwater Aerial Vehicle.
  • Type: Journal Articles Status: Other Year Published: 2023 Citation: Mengi, E., Becker, C. J., Sedky, M., Yu, S., and Zohdi, T. I. (2023) A Digital-Twin and Rapid Optimization Framework for Optical Design of Indoor Farming Systems.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Mengi, E., Samara, O.A., and Zohdi, T. I.(2023) Crop-driven optimization of agrivoltaics using a digital-replica framework. Smart Agricultural Technology. Volume 4, https://doi.org/10.1016/j.atech.2022.1001
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Zohdi, T. I. A machine-learning digital-twin for rapid large-scale solar-thermal energy system design, Computer Methods in Applied Mechanics and Engineering (2023) 115991, https://doi.org/10.1016/j.cma.2023.115991
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Goodrich, P., Betancourt, O., Arias, A., and Zohdi, T. I.(2022) Placement and drone flight path mapping of agricultural soil sensors using machine learning. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2022.107591
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Zohdi, T. I. (2022) A Note on Rapid Genetic Calibration of Artificial Neural Networks. Computational Mechanics. https://doi.org/10.1007/s00466-022-02216-4
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Isied, R. Mengi, E. and Zohdi, T. I. (2022) A digital twin framework for genomic-based optimization of an agrophotovoltaic greenhouse system. Proceeding of the Royal Society A. Volume 478, Issue 2267, https://doi.org/10.1098/rspa.2022.0414
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Zohdi, T. I. (2022) An adaptive digital framework for energy management of complex multi-device systems. Computational Mechanics. https://doi.org/10.1007/s00466-022-02212-8
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Zohdi, T. I. (2022) Machine-learning and Digital-Twins for Rapid Evaluation and Design of Injected Vaccine Immune Responses. Computer Methods Appl. Mech. Eng. https://doi.org/10.1016/j.cma.2022.115315
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Zohdi, T. I. (2022). A digital-twin and machine-learning framework for precise heat and energy management of data-centers. Computational Mechanics. https://doi.org/10.1007/s00466-022-02152-3
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Gao, Y.; Yang, J.; Qian, H.; Mosalam, K.M., Multiattribute multitask transformer framework for vision-based structural health monitoring, COMPUTER-AIDED CIVIL & INFRASTRUCTURE ENGINEERING, JUL 2023, Open Access. https://doi.org/10.1111/mice.13067.
  • Type: Books Status: Published Year Published: 2022 Citation: Zohdi, T. I. (2022) Modeling and Simulation of Infectious Diseases: Microscale Transmission, Decontamination and Macroscale Propagation. UCB link: https://link.springer.com/book/10.1007/978-3-031-18053-8
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: M�kiharju, S.A. Development of in-lab X-ray particle velocimetry and multispectral CT for multiphase flows 7th Cavitation and Multiphase flows Workshop, Chania, Crete, 6/16/2023. Keynote (one of 12 https://iicr-7.net/), workshop attended by global leaders in the field.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Released the first benchmark database for the use of food photos in dietary assessment, December 2022: Chin, Elizabeth L.; Larke, Jules A.; Bouzid, Yasmine Y.; Nguyen, Tu; Vainberg, Yael; Smilowitz, Jennifer. T.; Lemay, Danielle G. (December 2022). SNAPMe: A Benchmark Dataset of Food Photos with Food Records for Evaluation of Computer Vision Algorithms in the Context of Dietary Assessment. Ag Data Commons. https://doi.org/10.15482/USDA.ADC/1528346.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Larke JA, Bacalzo N, Castillo JJ, Couture G, Chen Y, Xue Z, Alkan Z, Kable ME, Lebrilla CB, Stephensen CB, Lemay DG. Dietary Intake of Monosaccharides from Foods is Associated with Characteristics of the Gut Microbiota and Gastrointestinal Inflammation in Healthy US Adults. J Nutr. 2023 Jan;153(1):106-119. doi: 10.1016/j.tjnut.2022.12.008. Epub 2022 Dec 26. PMID: 36913444; PMCID: PMC10196574. https://pubmed.ncbi.nlm.nih.gov/36913444/
  • Type: Journal Articles Status: Under Review Year Published: 2023 Citation: Larke JA, Chin EL, Bouzid YY, Vainberg Y, Nguyen T, Lee D, Pirsiavash H, Smilowitz JT, Lemay DG. Surveying Nutrient Assessment with Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Photos for Dietary Assessment. (in review)
  • Type: Journal Articles Status: Under Review Year Published: 2023 Citation: Youn, Jason, et al. "FoodAtlas: Automated Knowledge Extraction of Food and Chemicals Through Multimodal Deep Learning." Nature Communications, Under Review."
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Invited Presentation and Panelist, Artificial Intelligence in Nutrition: 2021 and Beyond Food For Fairness Summit, https://foodforfairness.org/ Held virtually
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Co-presented with Dr. Elizabeth Chin and Yasmine Bouzid AIFS Nutrition Project: Prediction of Glycan Content from Real-World Food Diaries Science Monday, Western Human Nutrition Research Center, Davis, CA
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Moderator for Immunity Tackles 21st Century Challenges and Invited Presentation: SPLASH!� Milk Science Update: 10th Anniversary: Highlights and Upcoming Topics (included A.I. segment) International Milk Genomics Consortium, Hybrid Symposium 2022
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Invited presentation, Eating for Trillions: How We Study Diet-Microbiome Relationships Discovery Forum: The Impact of Microbiota-Gut-Brain Axis in Human Health and Wellbeing. Robert Mondovi Institute, University of California, Davis.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Surveying Nutrient Assessment with Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Records Paired with Meal Photos (Oral). Nutrition Translation Award Competition, Finalist. American Society for Nutrition Boston, CA
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Surveying Nutrient Assessment with Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Records Paired with Meal Photos (Poster). Emerging Leaders in Nutrition Science Poster Competition, Finalist (Nutritional Epidemiology). American Society for Nutrition Boston, CA
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Wang, Xiaoxiao, et al. "Causal explanation for reinforcement learning: Quantifying state and temporal importance." Applied Intelligence (2023): 1-19.
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Wang, Xiaoxiao, et al. "Quantifying Causal Path-Specific Importance in Structural Causal Model." Computation 11.7 (2023): 133.
  • Type: Journal Articles Status: Under Review Year Published: 2023 Citation: Youn, Jason, Fangzhou Li, Gabriel Simmons, Shanghyeon Kim, and Ilias Tagkopoulos. "FoodAtlas: Automated Knowledge Extraction of Food and Chemicals from Literature." Nature Food. (Under Review).
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Aboud, Orwa, Yin Liu, Lina Dahabiyeh, Ahmad Abuaisheh, Fangzhou Li, John Paul Aboubechara, Jonathan Riess et al. "Profile Characterization of Biogenic Amines in Glioblastoma Patients Undergoing Standard-of-Care Treatment." Biomedicines 11, no. 8 (2023): 2261.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Youn, Jason, Fangzhou Li, and Ilias Tagkopoulos. "Semi-Automated Construction of Food Composition Knowledge Base." In 2nd AAAI Workshop on AI for Agriculture and Food Systems. (2023).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Youn, Jason, and Ilias Tagkopoulos. "KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction." In Proceedings of the 12th Joint Conference on Lexical and Computational Semantics. (2023).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Invited presentation, Eating for Trillions: How We Study Diet-Microbiome Relationships in Humans ARS Microbiome Seminar Series (nationwide, held virtually)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Invited presentation, Artificial Intelligence for Food and Nutrition Quadram 2023 International Collaboration: Biofortification, B12 and more: From nutrition to food biotechnology
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Youn, Jason, Navneet Rai, and Ilias Tagkopoulos. "Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes." Nature Communications 13, no. 1 (2022): 2360.


Progress 09/01/21 to 08/31/22

Outputs
Target Audience:For our second year of operations, we expanded our target audience to include both industrial and academic partners. More specifically, our target audience was: 1. Scientific community. In its second year, AIFS supported 6.3PIs, 59trainees (3undergraduate, 52graduate, and 4postgraduate or other trainees), 55of them in STEM areas. This group has published more than 24publications in peer-reviewed journals and conferences. There have been more than 40public talks through various outlets and organizations, including the National Academies, .... 2. Industry partners. Through our business development activities, we have engaged more than 40 companies in the area of AI and Food Systems. The target audience were principal scientists and engineers in food systems and data science, business development associates, leadership (CEO,CTO,CDO, etc.), among others. 3. Other stakeholders. These include (a) media sources (we provide content from our experts), (b) governmental and NGO groups, (c) broader community and practitioners, who want to use or learn about AI Ag and Tech, (d) entrepreneurs and investors, (e) service providers to farmers and the broader agricultural supply chain. In addition, our public engagement and education efforts also targeted: • Grad students/postdocs at AIFS organizations via AIFS education modules • AIFS researchers in general via AIFS internal seminars • Undergrads via the Career Exploration Fellowship Program and machine learning Bootcamp • General public, subcategories including academics and students outside AIFS, individuals in the industry, farmers/agricultural workers • Employees in general at participating universities and other institutes who see news feeds regarding our engagement work • Viewers of newsletters and social media posts. • Public attending conferences that we co-sponsor or provide moderators or speakers for Changes/Problems:There have been no major changes or problems in the approach. We note that due to COVID restrictions on travel, we did held off on any on-site conferences in which board members or the public would attend AIFS events during most of the project year. However, we planned and executied a joint annual meeting of board members and AIFS researchers alongside a public day of moderated panels and demonstrations the following day. This occured during May 2021.We have adapted to the COVID restrictions through virtual meetings and virtual seminars. We requested an NCE because of the ripple effect of the delayed start of projects during year 1. What opportunities for training and professional development has the project provided?In year 2, AIFS launched or continued many programs for training and professional development including: AIBridge: 1-week short course designed to bridge the gap between computer science and other disciplines. 16 participants (graduate students, undergraduates, and postdocs) leaned basic programming in Python and the concepts behind machine learning (ML). The focus was on applying ML to real research problems through hands-on labs and a final project presentation Career Exploration Fellowship Program: to prepare undergraduates from diverse backgrounds for careers at the intersection of food, agriculture, and technology by pairing them with companies, nonprofits, and university labs to work on projects for the academic year duration. 16 fellows from across the country were funded this year in the program's first cohort. Teacher Fellowship Program: summer professional development program for high school ag teachers on the practical uses of drones in curriculum. This program was designed to improve access, awareness, and interest among high school students in the applications of drones to agriculture. Six high school teachers developed a dynamic and effective instructional package ready for classroom adoption. Educational modules: 21 educational modules have been developed for grad students on topics in high-tech agriculture. AIFS offered technical training workshops in partnership with UC Ag and Natural Resources Informatics and GIS group Communication training: communication training workshops for oral presentations, videos, and posters, were offered to early career researchers participating in symposia and conferences throughout the year. How have the results been disseminated to communities of interest?AIFS conducted many outreach activities in year 2 for enhancing public understanding and disseminating research to communities of interest, including: AIFS Speaker Series: Featured engaging talks at the forefront of new AI solutions for the food system, designed to provoke thought, start conversations, and push research forward. All talks are publicly available on our YouTube Channel. DIGICROP 2022, The International Conference on Digital Technologies for Sustainable Crop Production: A virtual conference co-hosted with German Cluster of Excellence PhenoRob. The conference had 68 speakers for 37 institutions in 9 countries and 1,100 registrants from around the world. Early Career Researchers Symposium, co-hosted with PhenoRob: 20 students from the two institutions presented posters and video trailers AIFS Graduate and Postdoc Research Symposium: 12 graduate students and postdoctoral fellows shared their research virtually to an internal and external audience Annual Meeting and Showcase/Davis Discovery Days: AIFS hosted a day of public panels and speakers as part of this cross-campus event highlighting research Social Media/Newsletters: AIFS has 1,000+ followers on LinkedIn, 345 followers on Twitter, and its newsletters reach nearly 400 people Business Development outreach to potential industry partners Participation in key conferences and events: We shared our research and connect with others at academic and industry conferences and events such as Food for Fairness, Indoor AgCon, GreenTech, Future Food Tech Summit, and many more What do you plan to do during the next reporting period to accomplish the goals?AIFS researchers will collaborate across the three thematic focus areas to produce impactful reasearch across 10 projects addressing these themes. AIFS leadership will provide opportunities for connecting this research to novel uses in the food system+ AI space, and will convene regular speakers, speaking opportunities, presentations, and reviews to allow for the ability to gauge impact and progress along the way. To achieve its Education, Outreach, and Workforce Development goals, AIFS will continue to expand existing programs and launch new programs in Year 3. The Career Exploration Fellowship Program will return in summer 2023 for a new cohort of undergraduate students. The Teacher Fellowship Program will continue refining the curriculum modules for public release next summer. AIFS plans to expand educational module offerings to other demographics (undergraduates, community college students, industry professionals). We will teach AIBridge again and will also host a hackathon. We will continue hosting technical trainings conferences, and our speaker series. Additionally, we plan to continue growing our social media and web presence.

Impacts
What was accomplished under these goals? During its second year of operation, AIFS has concentrated its focus to three thematic areas: Food Systems Supply Chain Resiliency: Projects which leverage AI towards a more resilient Food System supply chain, especially those that lead to translational change and/or use cutting edge technologies (e.g., blockchain for avoiding adulteration, waste, and increasing food safety) , and the use of digital twins. AI-driven food systems to improve human health: Projects in this theme develop AI solutions for identifying, creating, cultivating, processing, and distributing nutritious, functional food, with the goal of improving consumer health. AI Infrastructure Development for Food Systems: The AIFS mission is to contribute to the development of such infrastructure, and proposals will need to clearly address an unmet need and have the potential to have a strong impact on the strategic vision of AIFS, and the community as a whole. In addressing these three themes, researchers applied for internal AIFS grants for projects which addressed at least one of these themes. 10 projects were funded, In order to foster collaboration, AIFS leadership encouraged successful proposals which involved collaboration between researchers, including between researchers at the various AIFS sites. AIFS reached out to the public, other researchers, and businessesvia the establishment of a strong presence on social media, with over 1000 followers on the professional website LinkedIn, an active Twitter Feed, and a Youtube Channel provided talks from the AIFS Speaker Series, which drew key speakers from all sectors of the food system. AIFS engaged over 40 businesses for discussions to introduce AIFS and its research capabilities in AI across the food system, and to encourage external funding of research. AIFS received an intent to award from the NSF for an international collaboration supplemental grant, allowing graduate students to engage wtih Waginengin University and Research, and Fraunhofer ISE. AIFS also established itself as a leader among AI institutes by being awarded a two year grant to plan and execute a to-be-annual Summit for AI Institutes Leadership (SAIL), as well as to develop an AI Virtual Organijzation, which involves the establishment of a web portal AIVO (https://aiinstitutes.org).

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: O. Betancourt. (8/2022) Optimized placement and drone flight path mapping of agricultural soil sensors using machine learning. AIFS Graduate Student and Postdoc Research Symposium. August 10, 2022.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Zohdi, T. I. (8/2021) Modeling and Simulation Tools for Industrial and Societal Research Applications: Digital Twins and Genome-based Machine-learning. ML4I Conference-Machine-Learning For Industry. Lawrence Livermore National Labs, August 12, 2021, Livermore, California (Invited Speaker).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Zohdi, T. I. (9/2021). A Digital-Twin and Machine-learning Framework for the Design of Multiobjective Agrophotovoltaic Solar Farms, September 30, 2021, Fraunhofer Institute for Solar Energy Systems ISE, Freiburg, Germany. (Invited Speaker)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Zohdi, T. I. (10/2021). Modeling and simulation tools for industrial and societal research applications. Melosh Judge Speaker. Durham, North Carolina, October 21, 2021 (Invited Keynote Speaker)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Zohdi, T. I. (11/2021). Digital-twin construction and genomic machine-learning optimization, Berkeley Institute for Data Science November 9, 2021 (Invited Speaker)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Zohdi, T. I. (2/2022). Modeling and Simulation Tools for Industrial and Societal Research Applications: Digital Twins and Genome-based Machine-learning, 2/2022, Columbia University , (Invited Keynote Speaker)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Zohdi, T. I. (6/2022). Modeling and Simulation Tools for Industrial and Societal Research Applications: Digital Twins and Genome-based Machine-learning, ECCOMAS, Oslo, Norway (Invited Semi-Plenary Speaker)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Zohdi, T. I. (8/2022). Modeling and Simulation Tools for Industrial and Societal Research Applications: Digital Twins and Genome-based Machine-learning, GoogleX, August 15, 2022 (Invited Speaker)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Zohdi, T. I. (8/2022). Instructional Modes for Modeling and Simulation Tools for Industrial and Societal Research Applications, Virtual Professional Development Conference, Khalifa University, UAE, August 22-26,2022 (Invited Keynote Speaker)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Zohdi, T. I. (10/2022). Building Digital Twins for Industrial and Societal Research Applications, Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS), Oct 10-11, 2022, (Invited Speaker)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Zohdi, T. I. (09/2020) Modeling and simulation of next-generation advanced manufacturing. SolMech 2022, Warsaw, Poland. (Invited Plenary Lecture)
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Barnett-Neefs C., G., Sullivan, C., Zoellner, M., Wiedmann, R., Ivanek (2022) Using agent-based modeling to compare corrective actions for Listeria contamination in produce packinghouses. PLoS ONE 17(3): e0265251. https://doi.org/10.1371/journal.pone.0265251
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Qian, C., Y. Liu, C. Barnett-Neefs, S. Salgia, O. Serbetci, A. Adalja, J. Acharya, Q. Zhao, R. Ivanek, and M. Wiedmann (2022) A perspective on data sharing in digital food safety systems, Critical Reviews in Food Science and Nutrition, DOI: 10.1080/10408398.2022.2103086
  • Type: Journal Articles Status: Under Review Year Published: 2022 Citation: Barnett-Neefs, C., M., Wiedmann, R., Ivanek. Examining Patterns of Persistent Listeria Contamination in Packinghouses using Agent-Based Models. Under review.
  • Type: Journal Articles Status: Submitted Year Published: 2022 Citation: Salgia S., S., Vakilli, Q., Zhao. Kernel-based Federated Learning with Personalization. Under submission.
  • Type: Journal Articles Status: Submitted Year Published: 2022 Citation: Serbetci O., S. Murphy, Q. Zhao, R. Ivanek. Statistical inference and graph centrality measures for food safety assessment based on digital-twin models. Under submission.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Ivanek, R. (2022) In Silico Models for Design and Optimization of Science-Based Listeria Environmental Monitoring Programs in Fresh-Cut Produce Facilities. Invited talk at ASM Microbe 2022, June 11, 2022.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Wiedmann, M. (2022) Breakfast Meeting: Data Sharing for Food Safety. Organized by the Institute for the Advancement of Food and Nutrition Sciences. At International Association for Food Protection (IAFP) Annual Meeting, Pittsburg, PA, July 31  August, 2022.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Barnett-Neefs (2022) Module on Agent-Based Models in Food Safety. https://www.youtube.com/watch?v=d5qKiVxy3Hk
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Barnett-Neefs (2022) Lecture on Building Agent-Based Models for Food Safety. https://www.youtube.com/watch?v=zzyhE6ey_AE
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Jung, Y., C., Qian, C., Barnett-Neef and M., Wiedmann (2022) Developing an Agent-Based Model to Assess Listeria Control Strategies in Retail Stores. Poster at International Association for Food Protection (IAFP) Annual Meeting, Pittsburg, PA, July 31  August, 2022.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Chao Huang, Jianwei Huang, and Xin Liu, ``Cross-Silo Federated Learning: Challenges and Opportunities'', submitted to IEEE Communication Magazine.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Chao Huang, Huanle Zhang,?Charles Kamhoua, Prasant Mohapatra, and Xin Liu, ``Incentivizing Data Contribution via Profit Allocation in Cross-Silo Federated Learning'', to be submitted to AISTATS.?
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Zohdi, T. I. (2022). A digital-twin and machine-learning framework for precise heat and energy management of data-centers. Computational Mechanics. https://doi.org/10.1007/s00466-022-02152-3
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Zohdi, T. I. (In press) Machine-learning and Digital-Twins for Rapid Evaluation and Design of Injected Vaccine Immune Responses. Computer Methods Appl. Mech. Eng. https://doi.org/10.1016/j.cma.2022.115315
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Zohdi, T. I. (2022) An adaptive digital framework for energy management of complex multi-device systems. Computational Mechanics. https://doi.org/10.1007/s00466-022-02212-8
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Castrillon, N., Rock, A. and Zohdi, T. I. (2022) Thermal Modeling and Uncertainty Quantification of Tool for Automated Garment Assembly. Computational Mechanics. https://doi.org/10.1007/s00466-022-02215-5
  • Type: Books Status: Awaiting Publication Year Published: 2022 Citation: Modeling and Simulation of Infectious Diseases, T. Zohdi; Gao, Y.; Zhai, P.; Mosalam, K.M., Balanced semi-supervised generative adversarial network for damage assessment from low-data imbalanced-class regime, COMPUTER-AIDED CIVIL & INFRASTRUCTURE ENGINEERING, 36(9):1094-1113, SEP 2021. https://doi.org/10.1111/mice.12741 [This paper received the 2021 Hojjat Adeli Award for Innovation in Computing, https://ce.berkeley.edu/news/2742]
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Autonomous Greenhouse Control: A Bayesian Optimization and Model-based Reinforcement Learning Approach, DIGICROP, Xin Liu, invited talk, Apr. 2022.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: R. Isied, E. Mengi. (3/2022) A digital-twin framework for machine-learning optimization of a greenhouse agrophotovoltaic system. DIGICROP 2022 - International Conference on Digital Technologies for Sustainable Crop Production. March 28, 2021. (Invited Speaker).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Zohdi, T. I. (8/2021) Modeling and Simulation Tools for Industrial and Societal Research Applications: Digital Twins and Genome-based Machine-learning. ML4I Conference-Machine-Learning For Industry. Lawrence Livermore National Labs, August 12, 2021, Livermore, California (Invited Speaker).
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Chao Huang, Shuqi Ke, and Xin Liu, ``Duopoly Business Competition in Cross-Silo Federated Learning'', submitted to IEEE INFOCOM.?
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Zohdi, T. I. (9/2021). A Digital-Twin and Machine-learning Framework for the Design of Multiobjective Agrophotovoltaic Solar Farms, September 30, 2021, Fraunhofer Institute for Solar Energy Systems ISE, Freiburg, Germany. (Invited Speaker)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Diepenbrock, Christine, Invited research talk, USDA NIFA Agricultural Genome to Phenome Initiative. Integrating crop models and whole-genome prediction in plant breeding pipelines.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Diepenbrock, Christine; Invited research talk, PhenoRob seminar series. Case studies in improving crop nutritional quality and abiotic stress tolerance.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Diepenbrock, Christine; Invited research talk, Univ. Florida Corteva Plant Science Symposium: Breeding for Climate Change: Stress Resilient Agriculture. Digital and genomics-enabled technologies to improve crop productivity and quality under abiotic stress conditions.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Diepenbrock, Christine; Invited virtual research talk and panelist, Seed Central (a cross-sector plant sciences community in the extended Davis area). Digital and AI technologies to improve crop nutritional quality and abiotic stress tolerance. Session focused on digital ag. and AI technologies for agriculture.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Runcie, D.E.; Invited research talk, DigiCrop, MegaLMM: A statistical model for genomic prediction with high dimensional traits.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Runcie, D.E.; Invited research talk, PAG ,Uncovering the genetic basis of local adaptation in maize with large-scale multi-environment trials.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Thacher, E., & M�kiharju, S. A. (2022). Effect of coherent structures on particle transport and deposition from a cough. Aerosol Science and Technology, 56(5), 425-433.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: R. Isied, E. Mengi, and T.I. Zohdi. A digital-twin framework for machine-learning optimization of a greenhouse agrophotovoltaic system. (Manuscript submitted for publication.)
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: eefs C, Sullivan G, Zoellner C, Wiedmann M, Ivanek R (2022) Using agent-based modeling to compare corrective actions for Listeria contamination in produce packinghouses. PLoS ONE 17(3): e0265251. https://doi.org/10.1371/journal.pone.0265251
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Rai, Navneet, Minseung Kim, and Ilias Tagkopoulos. Understanding the formation and mechanism of anticipatory responses in Escherichia coli. International Journal of Molecular Sciences (2022). doi: 10.3390/ijms23115985
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Youn, Jason, Navneet Rai, and Ilias Tagkopoulos. Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes. Nature Communications (2022). doi: 10.1038/s41467-022-29993-z
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Lee, Bo Mi, Ameen Eetemadi, and Ilias Tagkopoulos. Reduced graphene oxide-metalloporphyrin sensors for human breath screening. Applied Sciences (2021). doi: 10.3390/app112311290
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: de Oliveira, Eduardo Barros, Fernanda Ferreira, Klibs Galvao, Jason Youn, Ilias Tagkopoulos, Noelia Silva-del-Rio, Richard Van Vleck Pereira, Vinicius Machado, Fabio Lima. Integration of statistical inferences and machine learning algorithms for prediction of metritis cure in dairy cows. Journal of Dairy Science (2021). doi: 10.3168/jds.2021-20262
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Pereira, Beatriz Merchel Piovesan, Muhammad Adil Salim, Navneet Rai, and Ilias Tagkopoulos. Tolerance to glutaraldehyde in Escherichia coli mediated by overexpression of the aldehyde reductase YqhD by YqhC. Frontiers in Microbiology (2021). doi: 10.3389/fmicb.2021.680553
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Kim, Ki-Jo, Su-Jin Moon, Kyung-Su Park, and Ilias Tagkopoulos. Network?based modeling of drug effects on disease module in systemic sclerosis. Scientific Reports (2021). doi: 10.1038/s41598-020-70280-y
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Pereira, Beatriz Merchel Piovesan, Xiaokang Wang, and Ilias Tagkopoulos. Biocide-induced emergence of antibiotic resistance in Escherichia coli. Frontiers in Microbiology (2021). doi: 10.3389/fmicb.2021.640923
  • Type: Journal Articles Status: Submitted Year Published: 2022 Citation: Wang, X., Meng, F., Kong, Z., Chen, X., & Liu, X. (2023). Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance. Submitted to AAAI.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Use-Inspired AI in Next Generation Food Systems, Xin Liu, plenary talk, Monterey Data Conference, Aug. 31-Sept. 1, 2022.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Use-Inspired AI in Next Generation Food Systems, Xin Liu, plenary talk, DoE AI4SS Workshop, July 2022.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Autonomous Greenhouse Control: A Bayesian Optimization and Model-based Reinforcement Learning Approach, DIGICROP, invited talk, Apr. 2022.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: AI in Greenhouse Automation, Seed Central, Dec. 2021.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: C. Baumbauer, P.J. Goodrich, M. Payne, T. Anthony, A. Toor, D.J. Wong, C. Beckstoffer, W. Silver, and A. Arias, Printed Potentiometric Nitrate Sensors for use in Soil, Sensors, 22, 4095 (2022).
  • Type: Journal Articles Status: Submitted Year Published: 2022 Citation: P.J. Goodrich, O. Betancourt, A. Arias, and T. Zohdi, Optimized placement and drone flight path mapping of agricultural soil sensors using machine learning, submitted.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Ilias Tagkopoulos, Stephen F. Brown, Xin Liu, Qing Zhao, Tarek I. Zohdi, J. Mason Earles, Nitin Nitin, Daniel E. Runcie, Danielle G. Lemay, Aaron D. Smith, Pamela C. Ronald, Hao Feng, Gabriel David Youtsey. (2022) Special report: AI Institute for next generation food systems (AIFS) Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2022.106819
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Zohdi, T. I. (2022). A digital-twin and machine-learning framework for precise heat and energy management of data-centers. Computational Mechanics. https://doi.org/10.1007/s00466-022-02152-3
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2022 Citation: Zohdi, T. I. (In press) Machine-learning and Digital-Twins for Rapid Evaluation and Design of Injected Vaccine Immune Responses. Computer Methods Appl. Mech. Eng. https://doi.org/10.1016/j.cma.2022.115315
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Zohdi, T. I. (2022) An adaptive digital framework for energy management of complex multi-device systems. Computational Mechanics. https://doi.org/10.1007/s00466-022-02212-8
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Castrillon, N., Rock, A. and Zohdi, T. I. (2022) Thermal Modeling and Uncertainty Quantification of Tool for Automated Garment Assembly. Computational Mechanics. https://doi.org/10.1007/s00466-022-02215-5
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Zohdi, T. I. (2022) A Note on Rapid Genetic Calibration of Artificial Neural Networks. Computational Mechanics. https://doi.org/10.1007/s00466-022-02216-4
  • Type: Books Status: Awaiting Publication Year Published: 2022 Citation: Modeling and Simulation of Infectious Diseases, T. Zohdi
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: P.J. Goodrich, C. Baumbauer, A.C. Arias. A Passive-RFID Sensor Node for Precision Agriculture, International Conference on Precision Agriculture, Minneapolis, MN, June 26-29, 2022.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Ana Claudia Arias, Payton Goodrich, Carol Baumbauer, Anju Toor, Margaret Payne. BIODEGRADABLE POTENTIOMETRIC SENSOR TO MEASURE ION CONCENTRATION IN SOIL. Tech ID/UC Case 32471/2022-008-0
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: O. Betancourt. (3/2022) Optimized placement and drone flight path mapping of agricultural soil sensors using machine learning. DIGICROP 2022 - International Conference on Digital Technologies for Sustainable Crop Production. March 28, 2021. (Invited Speaker).


Progress 09/01/20 to 08/31/21

Outputs
Target Audience:The target audiences for AIFS include Related Scientific Community, including those internal to AIFS organizations directly and indirectly funded by this grant, as well as those outside of AIFS organizations who have existing have had new relationships formed as a result of AIFS research. University, Industry, Non-profit/community partners, and tech /ag users "in the field" both literally and figuratively speaking Students and future researchers Media sources (we providecontent from our experts) Governmental and NGO groups, including not only NIFA but board members' organizations which may be quasi-governmental, and NGO's) Lay community (people using / learning about AI Ag and Tech via social media and the internet) Business Dev and Investors (for future sustainability as an institute) Startups Tech and agricultural companies that sell to farmers Agricultural supply chain Our public engagement and education efforts also target: Grad students/postdocs at AIFS organizations via AIFS education modules AIFS researchers in general via AIFS internal seminars Undergrads viathe Career Exploration Fellowship Program General public, subcategories including academics and students outside AIFS, individuals in the industry, farmers/agricultural workers Employees in general at participating universities and other institutes who see news feeds regarding our engagement work Viewers of newsletters and social media posts. Changes/Problems:There have been no major changes or problems in the approach. We note that due to COVID restrictions on travel, we did not hold an annual conference or bring board members in for a meeting. COVID restrictions also delayed the hiring of some graduate students and one faculty. We have adapted to the COVID restrictions through virtual meetings and virtual seminars. We requested an NCE because mostof the 16 projects started in January 2021, after the internal RFP's were approved for funding. Since the projects were proposed with a 1-year duration, we expect to be evaluating final reports on those projects during October and November 2021. What opportunities for training and professional development has the project provided?Early in the year, the project has held 12 internal seminars by various researchers in groupings for each seminar, allowing all AIFS researchers to become familiar with each other's work. Additionally, we held a lightning talk event for all AIFS graduate students, over a span of several weeks. In preparation for this, the students attended a training session by AIFS' Education & Public Engagement Coordinator to teach them how to prepare a concise and interesting talk about their work. We have planned a science writing seminar in-person for all graduate students. Due to COVID restrictions, this even has been put on hold. The AIFS Education cluster has created 21 education modules which instruct various topics which are foundational to understanding an area of AIFS research. The scope and subject matter will be expended during subsequent years. How have the results been disseminated to communities of interest?As was described in the products section, AIFS researchers have presented 30 talks and published papers on topics related to AIFS research. Additionally, we have initiated social media presence on LinkedIn and Twitter, and have established a newsletter pipeline and database of recipients. Our Twitter and LinkedIn following is increasing geometrically thanks to recent articles and significant re-posts of our content. Alan Alda's Science Now group has been monitoring us, which indicates our extended reach and visibility. What do you plan to do during the next reporting period to accomplish the goals?During the second year of the grant, AIFS will be evaluating research-in-progress for the 16 year-1 research projects. Upon consultation with the executive board and receiving additional input from three external advisory boards (Science / Education, Industrial, Stakeholder), an RFP will be issued for year 2 work. The RFP may have language which directs the focus of existing projects and/or calls attention to new areas of focus. The proposals will be evaluated and funded. Some year 1 projects might continue, be expanded, contracted, re-focused, or finished. There will likely be new year 2 projejcts. AIFS will continue to expand its footprint among the food system via public talks, industry engagement, and media outreach. We will be bringing on board a Business Development Coordinator to help with engaging food system industries, literally from seed to fork, to identify opportunities for general collaboration and significant business partnerships. AIFS will also continue its dialog with similar institutes around the world and specifically existing and newly funded USDA-NIFA / NSF institutes. In order to nurture next-generation talent, we will be presenting a Fall/Winter/Spring AIFS Speaker Series, open to the entire food industry from line workers to researchers to decision makers, so as to foster an appreciation and interest in AI as a foundation for food system technologies and progress. We will also be developing additional educational modules focused on the workers in the industry.

Impacts
What was accomplished under these goals? Approximately 40 researchers and 40 graduate students have been working on AIFS related research during year 1 of the grant. This was coordinated via an internal RFP process during the first few months of the grant. The RFP required that projects overlap at least two of these research areas including core AI, molecular breeding, agricultural production, food processing & distribution, nutrition, and ethics & socioeconomics. 16 projects were funded. During this time, we have monitored project activity and requested that research publications and talks be regularly initiated, and USDA-NIFA credited. We have reached out and been reached out to by numerous companies and researchers. We have communicated with other NSF / USDA-NIFA institutes as well as sister institutes in Europe (PhenoRob and Fraunhofer) and have worked with each to set up information exchange events of various formats. Foundational Research Area 1: Use-inspired and Foundational AI One project addressing this research area is called "Data Efficiency". Research has beguntoIdentify key challenges in food systems using domain-specific examples in molecular breeding, agricultural production, food processing, nutrition. The research also seeks to develop active-learning-based approaches to address data efficiency in food systems, and to develop sim-to-real approaches that leverage simulations to improve data efficiency in machine learning algorithms with a small amount of real-world data in food systems. Trust is an essential prerequisite of adopting AI-based solutions in next generation food systems. Another research project titledExplainable Reinforcement Learning for Next Generation Food Systems aims at allowing food system AI products to explain conclusions and recommendations. The goal of this research is to develop astructural causal model (SCM) that describes the relationships between the variables (listed by the expert) that will be learned from the Reinforcement Learning (RL) agent's interaction with the simulator. Foundational Research Area 2: Socioeconomics and Ethics An AIFS Institutional Development Cluster has been established to coordinate the researchers' activities in this area. This cluster has identified relevant regulatory authorities at the state and Federal levels and built a database of regulatory entities and pertinent regulations at the California and national levels which ay impact AIFS' research, derivative work, or stateholders. This cluster's researchers have also been active in engaging the public, and have been scheduled to speak at an upcoming FoodForFairness summit featuring an AIFS panel, and opening remarks by the UC Davis Chancellor and aCongressman representing US district CA-3. Application Research Area 1: Molecular Breeding Projects titled "Multi-trait Breeding" and "Sensing and Modeling Leaf Biochemical and Physiological traits, including early vigor" have been initiated. The goals of the first project include the development of a simulation tool to rapidly simulate outcomes of breeding decision frameworks over multiple generations and calibrate the simulations around the current state of the UC Davis strawberry and pepper breeding programs. The goals of the Sensing and Modeling project include the development of non-destructive and simultaneous sensing of leaf biochemical and physiological traits at field and population scales Application Research Area 2: Agricultural Production Several projects within this cluster seek to address these challenges: Buildingdata-efficient AI models that fuse farmer knowledge with science and technology including ground-to-space sensor data, plant biology, and crop models. Advancingedge AI in remote agricultural environments with energy and memory-efficient hardware and software; and Designing for human-in-the-loop interactions to build constructive relationships between humans and AI technology. Some projects involve developing digital twin models (pathogen and precision ag, and robotic). The pathology digital twin project will create and validate a digital twin model simulating the spread of viral pathogens through droplets under simulated food facility conditions among other goals. The precision ag related digital twin project aimsto provide useful tools to enable rapid path planning for autonomous vehicle operators in real-time and to train operators in large surface area food systems. This work develops a digital twin and machine-learning optimization framework for model problems combining fluid-dynamics of released objects, ground-based vehicle or aircraft (unmanned or manned) dynamics, and energy-efficient path planning of multiple ground or air vehicles. Another project seeks to measure water and nitrogen stress. By developing an AI-enabled framework for near real-time monitoring and a prediction that are generalizable across many specialty crops, we expect this to be a transformative foundation for the food system. The project titled "AI-enabled yield sensing and forecasting for agricultural production" will involve the design, building, and deployment of yield monitoring sensors to accelerate ground-truth yield data collection leading to the development of a synthetic data generation pipeline that couples a 3D crop model to a deep learning framework for yield prediction. Application Research Area 3: Food Processing and Distribution Food loss is a significant issue in agricultural production. One of our projects, named "AI Prediction of Fruit Bruising, Serum Leakage, and Spoilage During Transportation of Processing Tomatoes to Processing Facilities", seeks to build an accurate AI regression model to predict tomato mold severity and likelihood of load rejection at processing facilities based on 10 years of data available from the Processing Tomato Advisory Board (PTAB) and the digital model of mold growth on the fruit based on physiological and environmental factors. Another project involves improving the thoroughness and measurable effectiveness of ultrasonic cleaning of produce using machine learning. Application Research Area 4: Nutrition The two projects within the nutrition cluster are: Predicting Health effects from Food Composition via Large-Scale Information Extraction And Predicting Gycan content The health effects project underway seeks to create a software package for the automated curation of nutrition data from published papers, and apply it to a corpus of hundreds of food and nutrition papers, and organize the resulting information in a Knowledge Base (KB). This KB to augment published datasets linking food intake to health outcomes to create a predictor of health effects from chemical composition of food. The Glycan project underway seeks to test off-the-shelf computer vision algorithms on food images for single core foods and then generate benchmark data set of real-world mixed meal photos paired with food records. We will then pilot test whether NIR can distinguish a pair of visually similar foods, and also develop glycan library for core foods.

Publications

  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Gao, Yuqing, Pengyuan Zhai, and Khalid M. Mosalam. "Balanced semisupervised generative adversarial network for damage assessment from low?data imbalanced?class regime." Computer?Aided Civil and Infrastructure Engineering 36.9 (2021): 1094-1113.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Lemay, Danielle G., et al. "Technician-Scored Stool Consistency Spans the Full Range of the Bristol Scale in a Healthy US Population and Differs by Diet and Chronic Stress Load." The Journal of Nutrition 151.6 (2021): 1443-1452.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Cui, Hemiao, et al. "Machine learning analysis of phage oxidation for rapid verification of wash water sanitation." Postharvest Biology and Technology 181 (2021): 111654.
  • Type: Journal Articles Status: Other Year Published: 2021 Citation: Olenskyj, A., Donales-Gonzalez, I., Earles, M., and Bornhorst, G. End-to-end Prediction of Uniaxial Compression Profiles of Apples during in vitro Gastric Digestion using 4D Micro-CT Imaging and Deep Learning. In preparation.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Zohdi, T. I. "A Digital-Twin and Machine-Learning Framework for Ventilation System Optimization for Capturing Infectious Disease Respiratory Emissions." Archives of Computational Methods in Engineering (2021): 1-13.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Zohdi, T. I. "A digital-twin and machine-learning framework for the design of multiobjective agrophotovoltaic solar farms." Computational Mechanics (2021): 1-14
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Eetemadi, Ameen, Ilias Tagkopoulos. Methane and fatty acid metabolism pathways are predictive of Low-FODMAP diet efficacy for patients with irritable bowel syndrome. Clinical Nutrition (2021). doi: 10.1016/j.clnu.2020.12.041
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Risner, Derrick, et al. "Preliminary techno-economic assessment of animal cell-based meat." Foods 10.1 (2021): 3.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Youn, Jason, Tarini Naravane, Ilias Tagkopoulos. Using word embeddings to learn a better food ontology. Frontiers in Artificial Intelligence (2020). doi: 10.3389/frai.2020.584784
  • Type: Journal Articles Status: Under Review Year Published: 2021 Citation: 1. Buxbaum, N., Leith, H., and M. Earles. High Resolution Non-Destructive Plant Biomass Monitoring via End-to-End Deep Learning. In preparation.