2019 CIDA Research Innovation Fund and Hatch Grant Award Winners

Congratulations to our CIDA Research Innovation Fund and Hatch Grant Award Winners!

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.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.