2019 CIDA Research Innovation Fund Award Winners for Summer Stipend

Updated – July 3rd, 2019

Congratulations to our CIDA Summer Stipend Award Winners!

Graduate Students

Ning Zhao (PhD candidate), COE

Primary Advisor – Fengqi You, COE; Secondary Advisor – Johannes Lehman, CALS

Title: Digital Tools for Systems Analysis and Data Analytics of Biomass Pyrolysis in Agriculture

Abstract:  Biomass is a promising source of renewable energy with eco-friendly characteristic receiving great attention especially in Digital Agriculture communities. Biomass pyrolysis is the thermal decomposition of biomass occurring in the absence of oxygen. Depending on the final temperature and reaction time, pyrolysis technologies can be classified into two types: slow pyrolysis and fast pyrolysis. The yields as well as the physical and chemical characteristics of the products vary considerably according to the types of pyrolysis technologies. This research project is to study the environmental impact of spatial analysis of biomass processing considering specific pyrolysis technologies under economic and environmental criteria. By performance life cycle and techno-economic analysis/optimization biomass supply chains, we leverage data science and systems engineering methods with agricultural systems. We adopt life cycle optimization method to balance both the techno-economic and life cycle environmental impact objectives. Two major activities of my work are life cycle assessment and systems modeling. In life cycle assessment, we evaluate the global warming potential (GWP) of different pyrolysis technologies using existing technology data and Ecoinvent V3.5. In problem modeling, a bi-objective optimization model is formulated in order to balance the economic and environmental objectives.

 

Jennifer Liu (PhD candidate), CIS, A&S

Primary Advisor – Steve Jackson, CIS; Secondary Advisor – Phoebe Sengers, A&S

Title: Internet of Dirt

Abstract:  In recent years, Internet of Things (IoT) infrastructures have expanded into the field of agriculture. Some of the applications for IoT in this context includes precision agriculture, livestock monitoring, and smart greenhouses. While these systems are designed in specific ways to fit their environment, they may degrade and fail in unexpected ways that require replacement and repair. In this research, I will study the maintenance of IoT infrastructures in the context of agriculture through 1.) conducting ethnographic field work and 2.) generating design implications for future IoT systems for agriculture. Over the course of the summer, I will examine the interactions between farmers, technicians, IoT infrastructures, and the environment as a way to understand how these systems are maintained across different field sites. These field sites will build upon existing collaborations with CIDA, and will include farms that have implemented IoT systems. The fieldwork methods will include participant observation, semi-structured interviews, and design probes as a way of gaining key insights on how these IoT systems are maintained. I will then generate design implications by identifying mismatches between practices at the field sites and current design directions as a way suggest how systems could better meet the needs of the farms. These design implications will support the development of future development of IoT systems for agriculture at local, regional, and global scales.

 

Magdalena Masello Souza (PhD candidate), CALS

Primary Advisor – Julio Giordano, CALS; Secondary Advisor – David Erickson, COE

Title: ReproPhone and e-Synch: Novel Tools to Automate and Optimize Cattle Reproductive Management

Abstract:  Main goals are to develop; 1) e-Synch; an intravaginal reusable device to automate synchronization of ovulation and monitor physiology of cows and 2) ReproPhone; a user-friendly, rapid, low-cost tool to determine pregnancy and ovarian status of cows on-farm. The e-Synch consists of an electronically controlled device loaded with products to synchronize ovulation and sensors to monitor cow activity and temperature. Briefly, user inserts e-Synch and selects desired treatment with a mobile app. Next, e-Synch automatically delivers hormones required for the selected protocol. Once protocol is completed, e-Synch is removed and the cow is inseminated. On the other hand, ReproPhone consists of a lateral flow immunoassay (LFIA) to quantify circulating reproductive hormones coupled with a portable imaging device connected to a mobile app. Specific activities: 1) Evaluate efficacy of e-Synch. Conduct field trials to evaluate the ability of current prototype to deliver hormones used for synchronization of ovulation. Based on initial tests we will work with Dr. Erickson’s group to improve the design and function of the device. 2) Optimize current LFIA. Currently our LFIA and reading system is designed to test plasma samples, which is not ideal for on-farm implementation, as samples need to be processed before testing. Therefore, I will work on modifying current format to measure hormones in unprocessed whole blood samples. Activities include laboratory experiments and on-farm testing.

 

Hannah Thomas (PhD candidate), CALS

Primary Advisor – Margaret Frank, CALS; Secondary Advisor – Bharath Hariharan, CIS

Title: Plant Vein Detection through Machine Learning and Computer Vision

Abstract: Advances in computer science have led to programs capable of identifying plant parts (e.g. flowers and fruit). This project will utilize these advances to tackle the challenge of using computer vision and machine learning to identify leaf venation in plants. In addition to moving water and sugars through the plant, veins transport macromolecules (RNAs and proteins). My thesis is to visualize this movement; this project will focus on imaging this process. Aim 1) To analyze the shape of venation in the leaf, a water-mobile dye will be added to the roots, taken up by the xylem and carried into the leaves. In a healthy plant, this happens very quickly. The resulting leaves are then imaged under a fluorescent microscope. Analyzing the results are difficult due to a high amount of noise in the images. Traditional segmentation fails to quantify the amount of fluorescence in the leaves. Aim 2) To solve this problem, we will create an automated imaging pipeline for detection of fluorescence in leaves. I will develop a computer vision approach with Professor Bharath Hariharan (CS). This will require the generation of a training set (produced in aim 1) that teaches the computer how to “see” veins. After training, the program performance will be tested against human annotated images. A successfully trained program will be able to automatically detect the presence of fluorescence within dynamic plant veins. This tool will be made publicly available at http://www.plant-image-analysis.org/