Archives: Projects

CIDA Project information supporting innovative, cutting edge ideas, the Research Innovation Fund (RIF) provides seed grants for cross-college collaborative projects.

Accurate and Affordable Genotype Imputation for Plant Breeding Based on Machine Learning

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Advances in technology are enabling the collection of vast amounts of genetic and phenotypic plant data. This data can help explain the genetics of traits important in agriculture and contribute to the development of sustainable food systems. The long-term vision underlying this project is to understand how genetics and the environment determine phenotypes important in agriculture and use this knowledge to develop methods and protocols for improved plant breeding. Central to this effort will be the development of techniques and frameworks in machine learning that enable modeling complex genetic datasets. If successful, these efforts will enable breeding crops that are more nutritious, require less water and fertilizer, and resist disease, ultimately improving human and environmental health.

Design and development of a multimodal sensing technology to characterize and quantify changes in suckle behavior in dairy breed calves that experience pre-weaning morbidity events

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There is growing interest in the use precision technologies for dairy calf management. However, there are currently few sensors commercially available and validated for use in this population. The objective of this project will be to develop novel and rugged multimodal sensors for integration with automated milk feeding stations; to measure and characterize suckle behavior including pressure sensing (pattern, duration, speed), oral contact temperature (at the point of maximum contact on the palate), and vibrations (created while breathing during suckling) in healthy and diseased pre-weaned dairy breed calves; and, to determine associations of changes in these parameters with morbidity and mortality outcomes in dairy breed calves prior to weaning. The ability to detect subtle physiological and behavioral derangement in group housed calves fed by automated milk feeders will improve early disease detection in dairy calves, inform decisions on disease intervention, monitor recovery, and improve overall calf health and welfare.

Optimized pathogen environmental monitoring program in food processing facilities through reinforcement learning and privatized federated learning algorithms

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The key challenges in pathogen environmental monitoring programs stem from the high cost in testing and experimentation, the high risk in contamination and outbreak, and the reluctance of individual facilities to share data due to privacy and liability concerns. This project uses Listeria monocytogenes contamination in food processing facilities as a model to develop new digital-twin models augmented with privacy-guaranteed machine learning solutions for food safety assessment. This proposed integrative framework will provide optimized allocation of testing resources, risk-averse prediction of effective corrective measures, and privacy guarantees to incentivize data sharing among stakeholders and be used as a model for future food safety systems.

Quantifying 3D vegetative growth and light environment for orchard trees with terrestrial lidar and computational modeling

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In orchard plantation systems, vegetative growth is a critical plant ecophysiological process that regulates tree architecture, carbon sequestration, and production of tree crops. Tree carbon allocation and growth patterns are determined by both genetic differences and tissue-level micro-environment. However, accurate and cost-effective quantification of tree vegetative growth has been challenging, especially at fine spatial scales (e.g. branch-level). In this project, we propose to use portable terrestrial laser scanning (TLS) to track fine-scale 3D changes in leaf area and woody volume for several cultivars of apple trees. The project will generate public and accessible workflow and software to quantify 3D tree architecture and vegetative growth and enhance our quantitative and predictive understanding of plant carbon allocation, which can guide sustainable management (e.g., pruning and optimizing carbon sequestration) for tree plantations and forests.

Advancing Autonomous Greenhouse Technology with AI for Sustainable Food and Plant Production in Controlled Environment Agriculture

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Controlled environmental agriculture (CEA) is a technology-based approach to sustainable food production using facilities like semi-closed greenhouses and exhibits advantages in terms of a potential higher productivity and quality, as well as robustness to external climatic conditions. CEA has been considered as a popular approach to food production at locations with harsh climate condition, in Space Farming and Urban Agriculture, and for the emerging portable plant factories within shipping containers. While the availability of skilled growers capable of managing high-tech greenhouses remains scarce, the increasing adoption of advanced Digital Tools enables the development of automatic and efficient operations in high-tech greenhouses to further improve the productivity and reduce the cost. This project aims to address technological challenges by integrating artificial intelligence (AI), sensing, mobile robots, and Internet of Things (IoT) tools and methods to develop a state-of-the-art digital platform for autonomous greenhouse to address an emerging and important problem that has the potential of paving the way of future farming practices that could be resilient to the climate, location, and space constraints.

Advancing Autonomous Greenhouse Technology with AI for Sustainable Food and Plant Production in Controlled Environment Agriculture

Written by Dlynne Plummer on . Leave a Comment

Controlled environmental agriculture (CEA) is a technology-based approach to sustainable food production using facilities like semi-closed greenhouses and exhibits advantages in terms of a potential higher productivity and quality, as well as robustness to external climatic conditions. CEA has been considered as a popular approach to food production at locations with harsh climate condition, in Space Farming and Urban Agriculture, and for the emerging portable plant factories within shipping containers. While the availability of skilled growers capable of managing high-tech greenhouses remains scarce, the increasing adoption of advanced Digital Tools enables the development of automatic and efficient operations in high-tech greenhouses to further improve the productivity and reduce the cost. This project aims to address technological challenges by integrating artificial intelligence (AI), sensing, mobile robots, and Internet of Things (IoT) tools and methods to develop a state-of-the-art digital platform for autonomous greenhouse to address an emerging and important problem that has the potential of paving the way of future farming practices that could be resilient to the climate, location, and space constraints.

The impact of COVID-19 on digital agriculture SMEs in Kenya

Written by Dlynne Plummer on .

The COVID-19 pandemic has been a test of the fragility of the global food and agricultural systems. Particularly, the pandemic heavily weighed down the agri-food systems in developing countries. Nonetheless, the outcomes of the pandemic are creating robust opportunities geared towards the sustainability and resilience of the global agri-food systems. In the agri-food value chain this surge in demand has accelerated the growth of digital agriculture SMEs. The foundation of this research project is built on exploring how the COVID-19 pandemic has impacted digital agriculture SMEs in agribusiness value chains of frontier markets through measuring things like disparities (e.g., gender differences) in subscriptions, the impact of COVID-19 on sales and turnover of SMEs, and the socio-economic and political environment.

Accelerating the application and adoption of remote sensing decision support in Northeastern viticulture

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This project proposes to accelerate the application and adoption of remote sensing in Northeastern viticulture through an approach focused in research and extension with large implications for the NYS grape-growing region and the empowerment of local farmers. Remote sensing is a widely popular tool in most major grape growing regions of the world but has yet to be adopted in the eastern U.S. Using a combination of high resolution (sub-1m) satellite imagery, hyperspectral solar induced fluorescence (SIF), and synthetic aperture radar (SAR) sensing data the project will develop new methodology for crop and disease management in conjunction with CCE and in partnership with eight local growers. Research results will guide the project vision to develop an integrated decisions support system informing early crop and disease management intervention.

Automating management of teat tissue condition in dairy cows through machine learning

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Mastitis, a significant concern in the dairy industry due to marked reduction in milk production and the reduced immune system for dairy cattle, is assessed through physical examinations of a dairy cow’s teat health. This costly, time-consuming approach is error prone and labor intensive. This project proposes first to use a multi-modality machine learning system to measure for short term changes in teat tissue while also developing an image and video based deep learning classifier to predict long-term changes. The sensitivity and specificity of the machine learning system will be compared with the traditional, manual approach. Results will potentially impact current milk harvesting strategies, the advancement of udder health, animal well-being, and sustainability of farms with local impact in the NYS dairy industry and across the globe.

Sociology of Myanmar’s Emerging Agritech

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This project focuses in emergent agritech initiatives in Myanmar, a nation that came online only after a democratizing government shattered the military telecoms monopoly in 2014. The research contributes to the growing fields of digital political ecology, digital agriculture, and critical data studies by ethnographically investigating how new farmer extension apps, drone-spraying services and digital cooperatives are reshaping relationships between people, food, and land. Building on interviews and participant observation, a case study will be developed into a manuscript to be submitted to the Global South to Agriculture and Human Values or Geoforum in Fall 2020.

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