Inspiring new frontiers in science, policy and practice.

Research Innovation Fund Seed Grants

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

2020:

Accelerating the application and adoption of remote sensing decision support in Northeastern viticulture
Gold (CALS), R. Lohman (COE), J. Heuvel (CALS), Y. Sun (CALS), T. Bates (CCE), J. Meyers (CCE), J. Russo (JCB), A. Wise (CCE), H. Walter-Peterson (CCE), Y. Jiang (AgriTech)

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.

In Situ Nuclear Magnetic Resonance Monitoring for Improving Cassava Root Quality
El-Ghazaly (COE), M. Gore (CALS)

The quality of most root and tuber crops is based on the amount of dry matter content and relates inversely to the amount of water content. This research proposes to apply a novel approach to above-ground measurement techniques through the use of a non-destructive surface nuclear magnetic resonance (SNMR) system. This less invasive approach will measure water and dry content in cassava in different settings – laboratory, greenhouses, and fields – as well as across the growing cycle. Additionally, the project will explore increasing the sensitivity of the SNMR receiver and reducing the drive current required for the SNMR transmitter in order to reduce the size and power consumption of SNMR systems and make field deployment viable for farmers and researchers alike.

StraBot: a Soft, dexterous soft manipulator with hybrid sensing for strawberry harvesting and monitoring 
Shepherd (COE), M. Pitts (CALS)

Automating strawberry picking is the holy grail of agriculture. It is backbreaking work, the point at which you pick the strawberry is highly variable, and requires delicate touch when pulling. Soft robotics is a technology that has been developed over the past decade and can potentially solve this problem. Dr. Pritts is an expert in strawberry agriculture, and Dr. Shepherd is an expert in soft actuation and sensing. This work will finally explore the potential for soft robotics to solve this critical agricultural need by creating robotic manipulators that can pluck strawberries at high yield.

Automating management of teat tissue condition in dairy cows through machine learning
Basran (CVM), K. Weinberger (CIS), I. Porter (CVM), M. Wieland (CVM), J. Giordano (CALS)

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.

2019:

Improving strawberry yield through native and robotic pollinators
Kirstin Petersen, Assistant Professor, College of Engineering (COE), Electrical and Computer Engineering (ECE); and Scott McArt, Assistant Professor, College of Agriculture and Life Sciences (CALS), Entomology.

The proposed work will integrate automated monitoring of wild and managed pollinators with cutting-edge robotic pollination, laying the groundwork for a bio-hybrid system capable of observing, predicting, and improving yield in pollen-limited crops. Specific innovations include durable, low power insect camera traps, mobile end-effectors for local electrostatic pollination, rapid cross-pollination by quadcopters, and growth models conveyed to the farmer through an online app. These technologies will be validated with strawberry plants over several bloom cycles in the greenhouse, and through field experiments in a commercial farm. Short term, these technologies can be seamlessly integrated into current farm practices. Long term, they may be managed by automated schedulers to ensure optimal yield long before harvest. In a broader sense, this research opens a new frontier in precision agriculture, where robots not only have the intelligence to overcome the challenges of field deployment, but can operate as part of the natural ecosystem around crop plants.

New soil robotics and sensing for soil-root phenotyping of water-use effectiveness
Taryn Bauerle, Associate Professor, CALS, School of Integrative Plant Science (SIPS); Robert Shepherd, Associate Professor, COE, Mechanical and Aerospace Engineering (MAE); Mike Gore, Ph.D. ’09, Associate Professor, CALS, SIPS; Johannes Lehmann, Professor, CALS, SIPS; and Abraham Stroock ’95, Professor, COE, Chemical and Biomolecular Engineering (CBE).

Soil, the microbiome, and plant roots represent a critical frontier in agricultural science and practice. The opacity, heterogeneity, and dynamic nature of soils have severely limited in situ studies, phenotyping, and precise interventions as part of soil and crop management. Here, we will develop two innovations to access real-time information about the availability and flow of water in the rhizosphere: 1) a sensing strategy to provide sub-millimeter resolution of water relations (potential, content, and conductance) within the rhizosphere, in situ; and 2) a soil-swimming robot to provide semi-autonomous exploration of the root zone with multiple sensing modalities. We will pursue experiments with our emerging capabilities guided by scientific questions about roots and rhizosphere to drive new approaches to field-based phenotyping and management of irrigation and fertigation. The technology will lead to improved management of grain, horticultural, ornamental and tree cropping systems. Our project emphasizes a systems-based, trans-disciplinary approach and seeks to enhance and apply new innovations and technology to include belowground phenotyping (e.g. rhizosphere plant-soil interactions), sensor technology (e.g., real time soil water flux), robotics (e.g., spatio-temporal environmental sampling).

Microbiome-informed computational models and decision support tools to predict fresh produce spoilage: spinach as a model system
Martin Wiedmann, Ph.D. ’97, Professor, CALS, Food Science (FS); and Renata Ivanek Miojevic, Ph.D. ’08, Associate Professor, College of Veterinary Medicine (CVM), Population Medicine and Diagnostic Sciences.

Microbial food spoilage is a significant economic, environmental and societal problem: 40% of food in the US is reported to go to waste, with 2/3 of this spoilage being estimated to result from unwanted microbial growth. The goal of this project is to develop a computational model of microbiome interactions and perturbations during processing, transportation and retail for predicting shelf life of fresh spinach. Prediction of food spoilage in the food industry to date is typically based on limited laboratory experiments and shelf life studies conducted under a single or very few conditions. Actual product however is produced and distributed under a range of very different conditions throughout the supply chain. Hence, there is a need for transformative solutions to reduce food waste using a systems approach, in which innovative technologies are integrated across each stage of the supply chain to reduce the volume of food wasted. In this study, we will construct computational models and decision support tools to predict shelf life using both classical microbiological and metagenomics data. This work will serve as a basis to later develop and pilot transformational strategies to reduce food waste through more accurate shelf life prediction.

Accelerated and automated stress diagnostics in apple orchards
Awais Khan, Associate Professor, CALS, SIPS at Cornell AgriTech; Serge Belongie, Professor, Computing and Information Science (CIS), Computer Science (CS) at Cornell Tech; and Noah Snavely, Associate Professor, CIS, CS at Cornell Tech.

Apple orchards suffer from large numbers of diseases that can incur serious damage to trees, fruits, and the industry. Effective disease control methods rely on accurate, early diagnostics to implement successful and environmentally-sound management. As disease symptoms vary widely due to age of infected tissues, genetic variations, and light conditions within trees, it is challenging for computer vision models to accurately distinguish between the symptoms of different diseases. Our team of plant pathologists, phenotyping experts and computer vision scientists will develop computer vision models to accurately distinguish between the symptoms of many diseases that can incur serious damage to fruits and fruit trees. We will develop user-friendly apps to enable extension educators and consultants to support growers, and empower them to independently scout their orchards and provide accurate early diagnostics as the basis for successful and environmentally-sound disease management. Based on this work, we aim to lead a global challenge competition in the Fine Grained Visual Classification (FGVC) workshop at the Computer Vision and Pattern Recognition 2020 conference to find novel solutions to major challenges in computer vision.

Carbon farming: Combining machine intelligence, big data and process models to support this emerging sector
Dominic Woolf, Senior Research Associate, CALS, SIPS; Johannes Lehmann, Professor, CALS, SIPS; and Fengqi You, Professor, COE, CBE.

Restoration of soil organic carbon plays a critical role in addressing climate change while improving agricultural efficiency and reversing land degradation. However, scaling up of soil carbon sequestration is impeded by the high cost of monitoring, and by high levels of uncertainty in soil carbon predictions. Current soil organic carbon maps are based only on spatial interpolation of geographic, environmental, and climatic co-variates. As such, they do not distinguish the impacts of land management, including factors such as tillage regimes, crop rotations, crop and varietal selection, residue management, manure management, irrigation, cover crops, soil and water conservation etc. To provide an improved soil carbon maps that include these factors, we will train and validate machine learning and deep learning models using detailed spatial data on soils, vegetation, climate, and cropping practices. This project aims to create a step change in the accuracy of prediction of soil organic carbon by combining Cornell’s state-of-the-art soil mechanistic modeling with machine learning, deep learning, and spatially-explicit big data to create a “grey-box digital twin”. This will provide a platform to drive evidence-based policy and support massive scaling up of optimized investment in soil health and climate-change mitigation.

Function-targeted high-resolution phenotyping platform to deduce genetics-functions relationships in rhizomicrobiome for promoting plant nutrients utilization
April Gu, Professor, COE, Civil and Environmental Engineering (CEE); Jenny Kao-Kniffin, Associate Professor, CALS, SIPS; and Kilian Weinberger, Associate Professor, CIS, CS.

Rhizo-microbiome research is in its infancy and holds the key to a better understanding of plant-microbe interactions with a positive impact on plant health, productivity and agricultural sustainability. This study will leverage innovative single-cell Raman microspectroscopy (SCRM) technology and computational science to develop a novel and integrated phenotyping-genotyping technology platform as the basis for building a world-class agricultural phenotyping facility at Cornell. The complexity of the SCRM dataset, due to its size and composition, with low abundance of mostly unknown species (as expected for rhizomicrobiome), demands complicated dimension reduction and classification methods to achieve the desired performance. The goal is to discover and profile new and in situfunctionally-relevant microorganisms, such as polyphosphate accumulating organisms (PAOs) that contribute to P utilization, and carbon (PHB, glycogen)-accumulating organisms (CAOs) involved in nitrogen fixation, among others, and to establish gene-function relationships by correlating phenotypic and genotypic profiles. Discoveries made on this project will advance the technological, biophysical and socio-economic knowledge needed to shift the paradigm towards better management of land and water resources to ensure food, energy and water security in intensified agricultural regions such as New York State.

Scalable digital sensors of the skies and soils: An internet of things approach to improve farm-scale weather forecasts of extreme heat, drought and rainfall
Toby Ault, Assistant Professor, COE, Earth and Atmospheric Sciences (EAS); and Max Zhang, Associate Professor, COE, MAE.

Extreme weather is a serious threat to agriculture, economic vitality, human safety, and physical infrastructure in farming communities throughout the world. Climate change is likely to increase the risk of severe weather, particularly heat waves, droughts, and floods. To flourish in spite of these hazards, farmers, growers, agro-business, and food producers require a toolkit of political, infrastructural, and technological resources to manage the risk of extremes. Numerical models of weather and climate will be among the most important of these tools because they empower decision makers with information to anticipate and prepare for consequential events. The proposed research will monitor and forecast key variables for predicting extreme weather at State, County, and Farm scales in the Northeast by leveraging an existing wireless “Internet of Things” (IoT). We will develop open source tools for numerical weather and climate prediction to empower decision makers with information to anticipate and prepare for consequential events, and to provide farmers, growers, agro-business, and food producers with a toolkit for predicting key hazards to agriculture, particularly during the warm season when extreme rainfall, heatwaves, and droughts often exact severe crop losses.

Development of predictive models to accurately detect subclinical and clinical mastitis in dairy cows milked with automated milking systems
Rick Watters, Director, Quality Milk Production Services Western Laboratory and Senior Extension Associate, CVM; and Kristan Reed, Assistant Professor, CALS, Animal Science (ANSC).

Inflammation of dairy cow mammary glands, or mastitis, is one of the most important diseases in dairy production. Costs related to veterinary service, labor, loss of saleable milk, reduced milk production, and culling make it one of the most costly dairy diseases. In conventional milking systems, detection of clinical mastitis (CM) is straight forward by identification of abnormal milk or a swollen quarter, but subclinical mastitis (SCM) is only identified by a somatic cell count (SCC) ≥ 200,000 cells/mL. Accurate and timely detection of SCM has the potential to improve milk quality and farm economics. We propose to develop predictive models to accurately detect clinical mastitis (CM) and subclinical mastitis (SCM) in dairy cows milked with automated milking systems. These systems automatically provide hundreds of data points from each cow at milking. We will collect quarter level data points, such as milk yield, milking time, duration between milking visits, kick-offs, incomplete milkings, and conductivity, and use them to develop an algorithm for accurate identification of cows at the onset of CM and SCM. Not only will this provide a substantial economic benefit by reducing the costs associated with mastitis, but it will also improve animal welfare. While automated milking systems are still rare in the dairy industry, there is a projected annual increase of 20 to 30% in the coming years, in part as a way of addressing the current labor challenges in the dairy industry. New York State is seen as a leader in the dairy industry both locally, regionally, and globally and development of an algorithm for accurate identification of cows with CM or SCM will keep New York State on the leading edge of adoption of agriculture technology.

Remote-sensing based framework for farm-scale in-season crop yield forecast
Ying Sun, Assistant Professor, CALS, SIPS; Carla P. Gomes, Professor, CIS, CS; Ariel Ortiz-Bobea, Assistant Professor, SC Johnson College of Business (JCB), Dyson School of Applied Economics and Management (Dyson).

This proposal aims to develop scalable, field-scale, in-season forecast approaches for crop yield at low cost by synergistically integrating new technological advances including UAV/satellite remote sensing of Solar-Induced Chlorophyll Fluorescence (SIF) and hyperspectral reflectance, the state-of-art mechanistic crop growth models, and machine learning techniques. We propose to develop both process- and statistics-based approaches for yield forecast and examine their complementary strengths for large-scale operational application. We will finally build a Google Earth Engine based web portal to report yield forecast on weekly basis to inform farmers, agribusinesses, and extension agents in near real time. We will seek input and feedbacks of our developed framework from local farmers via Cornell Cooperative Extension and New York Corn and Soybean Grower Association during the course of the project.

2018:

Controlled Environment Agriculture in Metropolitan Areas
Neil Mattson, Associate Professor, CALS, SIPS; Miguel Gomez, Associate Professor, JCB, Dyson; and Anusuya Rangarajan, Director, Cornell Small Farm Program, CALS, SIPS.

Controlled environment agriculture (CEA), such as greenhouses, plant factories, and vertical farms, may be a viable alternative to conventional field-based production of vegetables for supplying metropolitan areas. This project will develop tools to assess the economic viability and sustainability of CEA operations, and guide their development in urban areas.

2017:

Using Touch Sensitive Soft Robots to Improve Vineyard Management 
Kirstin Petersen, Assistant Professor, COE, ECE; and Justine Vanden Heuvel, Associate Professor, CALS, SIPS.

This project aims to develop an automated vineyard system to accurately determine vine yield, leaf area to fruit ratio, and cluster integrity based on touch-sensitive soft robots (rather than the industry standard of computer vision), accurately estimating grape vine yields prior to harvest.

E-synch: Improving Cattle Reproductive Management 
David Erickson, COE, MAE; and Julio Giordano, Associate Professor, CALS, ANSC.

The easy-to-use E-synch device will reduce the hassle of reproductive management of dairy cows, using sensors to closely monitor individual animals and customize the protocols for their reproductive cycle in real-time. An electronically controlled, reusable device will deliver reproductive hormones automatically. This project aims to help balance and optimize the productivity and health of millions of cows around the world.

Using Micro Water Sensor to Manage Irrigation in Apple Orchards
Lailiang Cheng, Professor, CALS, SIPS; Alan Lakso, Professor Emeritus, CALS, SIPS; and Abraham Stroock, ’95, Professor, COE, CBE.

This project is developing an integrated digital solution for optimizing irrigation in apple orchards, using a micro water stress sensor technology – the microtensiometer – recently developed by Cornell. This tiny and inexpensive chip combined with smart technology systems collecting and interpreting the data it provides, will serve as a foundation for the next generation of automated irrigation systems, delivering water only where it is needed.

What Keeps Farmers from Adopting Digital Agriculture? 
Solon Barocas, Assistant Professor, CIS, Information Science (Info Sci); Karen Levy, CIS, Info Sci; and Harold van Es, CALS, SIPS.

This study aims to uncover the concerns farmers might have when deciding to adopt new precision agriculture technologies, focusing on how novel forms of data collection and information flow have raised questions about privacy and the distribution of the resulting economic benefits.

Smart Agriculture Through Big Data-driven Optimization 
Fengqi You, Professor, COE, BCE; and Matt Ryan, Assistant Professor, CALS, SIPS.

The connections between agriculture, economics and sustainability are complex, and so are the ever-increasing streams of available data. This project aims to advance climate-smart farming, optimize crop insurance and promote conservation agriculture by combining data-driven optimization and advanced machine learning techniques with digital agriculture data.

Facilitating Access to Complex Climate and Weather Data 
Arthur DeGaetano, Professor, CALS, EAS; and Madeleine Udell, Assistant Professor, CIS, Statistics and Data Science (DSDS).

The Northeast Regional Climate Center (NRCC) has developed the Applied Climate Information System (ACIS) that allows users to easily access a wide range of weather observations, climate projections and weather forecasts. NRCC staff is developing an array of decision tools, including a database of past weather forecasts to allow researchers and users to assess and improve the accuracy of forecasts. The team focuses on improving resolution of data and regional relevance.

Intelligent Lighting Systems in Greenhouses 
Neil Mattson, Associate Professor, CALS, SIPS.

The Greenhouse Lighting and Systems Engineering (GLASE) consortium is advancing LED light engineering, plant photobiology, carbon dioxide enrichment and systems control to create intelligent systems that can dramatically reduce the energy cost and carbon footprint of horticultural lighting.

Using Drone NDVI Imaging to Manage Nitrogen and Yield 
Quirine Ketterings, Professor, CALS, ANSC; and Elson Shields, Professor, CALS, Entomology.

This project is evaluating the use of drone-generated NDVI (normalized difference vegetation index) maps as tools for predicting yield and nitrogen needs of corn and forage sorghum. The team is developing a standard operating procedure for using drones to collect NDVI imagery to ensure consistent, actionable data under changing light and growing condition.

Smart Tools for Apple Growers to Protect Crops 
Art DeGaetano, Professor, CALS, EAS.

Apple freeze-risk decision tool helps apple growers to protect their crops against spring freezes: When a frost hits after a warm spell, apple producers begin to see damage to the developing fruit. The easy-to-use, online tool considers the exact location, the apple variety, and the stage of bud/bloom development to assess the risk of freeze damage.

Imaging and Analyzing of Leafy Plant Diseases 
Sarah Pethybridge, Associate Professor, CALS, SIPS at Cornell AgriTech, Geneva, NY .

Leaf Doctor and Estimate, two new free apps, work with photo analysis of damaged leaves to determine plant disease severity and help growers and researchers to decide if and how to treat the plant. While Leaf Doctor analyzes photos users take, Estimate connects users to a database of diseased leaves to help determine damage.

Digital Mapping Technology for Grape Growers 
Terry Bates, Senior Research Associate, CALS, SIPS at Cornell Lake Erie Research and Extension Laboratory, Portland, NY.

Digital mapping technology for grape growers: the project aims to bring precision viticulture technology to grape growers, by measuring conditions related to soil, canopy and crop, and using software developed by the research team to produce detailed digital maps.

Using Proximal NDVI Sensors to Increase N Use Efficiency
Quirine Ketterings, Professor, CALS, ANSC.

Algorithms convert NDVI measurements of hand-held or tractor mounted sensors into on-the-go N rate recommendations while in the field. This project evaluates which algorithms are most appropriate to use for corn grown for silage or grain in New York State.

Using Drone Imagery to Guide Selective Harvest in Vineyards 
Justine Vanden Heuvel, Associate Professor, CALS, SIPS.

The practice of selective harvesting for different grades of fruit quality in wine grape vineyards is common among large producers. This project helps Finger Lakes wine grape growers to learn how to use drones to collect NDVI images of their vineyards, and use them to guide harvest plans and maximize the economic potential of their fruit.

Improving Dairy Cow Health and Reducing Labor Cost 
Julio Giordano, Associate Professor, CALS, ANSC.

This project aims to better understand the behavioral, physiological, and productivity parameters during health and disease in dairy cows. The team’s experiments on dairy farms are designed to understand if automated health monitoring can promptly and accurately identify cows suffering from health disorders.

Improving Apple Grower Profitability Through Precision Management Smart App 
Jaume Lordan Sanahuja post doc CALS, SIPS; Poliana Francescatto, post doc CALS, SIPS.

The aim of this project is to develop an innovative, fast and easy-to-use tool for growers, making the precision management practices of apple orchards easier to accomplish. A new app will provide data analysis and real-time guidance, and help boost grower productivity in an environmentally friendly way.

Adapt-N: Web-based Nitrogen Management Tool 
Harold van Es, Professor, CALS, SIPS.

The Adapt-N provides precise N fertilizer recommendations to farmers for corn crops, accounting for the effects of seasonal conditions and field-specific information on crop and soil management. Adapt-N is now licensed to Yara International, and won the $1 million grand prize from the Tulane Nitrogen Reduction Challenge.