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Number of Awards:

TBD

Value, duration, matching requirements

$6,000 from NSERC for 14-16-weeks, full-time basis. Professors are required to supplement the award, in compliance with provincial employment standards. This translates into a ‘top-up’ of at least $3500 over 16 weeks.

Eligibility Requirements:

§  Canadian citizen or permanent resident.

§  Registered in a bachelor’s degree program at the time of application

§  Must have completed all the course requirements of at least the first year of university study (or two academic terms) of your bachelor’s degree.  Students graduating in May are eligible to hold an award.

§  Have obtained, over the previous years of study, a cumulative average of at least second class (a grade of "B" or "B-," if applicable) as defined by your university.

§  You may hold only one USRA per fiscal year (April 1 to March 31)

§  You may hold a maximum of three USRAs throughout your undergraduate career (regardless of the granting agency).

Additional details on USRA eligibility and application process are on the 

Number of Awards:

TBD

Value, duration, matching requirements

$6,000 from SAURA for 14-16-weeks, full-time basis. Professors are required to supplement the award, in compliance with provincial employment standards. This translates into a ‘top-up’ of at least $3500 over 16 weeks.

Eligibility Requirements:

§  Enrolled in a degree or diploma undergraduate program at the Faculty of Agriculture. Preference to students enrolled in the Honours stream.

§  A cumulative GPA of at least 3.7 (A-).

§  Students in a degree program will have completed at least 30 credit hours of course work. Students in a diploma program will have completed at least 20 credit hours of course work and be enrolled in a Bachelor program for the semester following receipt of the SAURA.

Dr. Vasantha Rupasinghe
Dept of Plant, Food, and Environmental Sciences
vrupasinghe@dal.ca

Project title: Food as Medicine

We have a few active projects investigating the health-promoting biological properties of probiotic-fermented food as well as postbiotics. The undergraduate students will work with the support of graduate students and/or postdoctoral fellows of the Food Bioactive Research Program.

Dr. Ahmad Al-Mallahi

Project title: Development of rock mapping system for rock picking robotic arm

Removing rocks off an agricultural field is a typical farm operation that takes place at the beginning of each season, which is accomplished usually manually. Due to ageing and the shortage of labourers, there is a need to mechanize this operation. In this project, the objective is to develop a smart mechanism to locate rocks in a field before dispatching a robotic arm to pick the rocks. At the first stage of this project, a camera will be programmed and mounted on a potato planter to scan the field as the planter drops and buries potato under soil. The images taken by the camera will be processed to detect the rocks. Meanwhile, GPS data collected from the planter will be matched with the processing results to create a rock distribution map.  This work will take place at the DAL-AC campus as well as at a commercial potato field in New Brunswick. The field work will include collecting samples of rocks and bringing them back to campus for analysis and the beginning of a design of an end-effector to grip the rocks and lift them. Students interested in this project will work with a team of graduate students at the Department of Engineering. The requirements for this project are basic knowledge of computer programming, the basics of electric circuits, and Engineering drawing using CAD or other software.

Project title: Backend computation of nutrient concentration in plants using nutrient sensor

In-season concentration of nutrients in plants is an essential piece of information to determine the fertilization scheme. The objective of this project is to develop a sensor to detect nutrients in plants based on spectroscopy to replace manual and chemical measurements which take weeks to deliver results. This project includes scanning potato plants in field using a spectrophotometer connected to the internet, developing nutrient sensing models plugged in a computation cloud, and delivering nutrient results to mobiles via an App. The research work in this project includes collecting samples of leaves and petioles of potato plants to build up datasets, testing and validating the sensing models plugged on the cloud, and streamlining the process of data delivery via the cloud to the mobile. This work will take place at the DAL-AC campus as well as at a commercial potato field in New Brunswick. The field work will include collecting leaf and petiole samples and handling them, using a spectrophotometer to scan dried and fresh leaves, and development of real-time results notification system to a mobile via the cloud. Students interested in this project will work with a team of graduate students at the Department of Engineering as well as the R&D Department at McCain Foods. The requirements for this project are basic knowledge of computer programming and the basics of statistics. Prior experience working in an agriculture field will be considered as an asset.

Project title: Machine vision to detect beetles and larvae in potato fields for autonomous spraying

Spot application is a term used in agriculture when the sprayer is able to detect solely the pest and avoid spraying unnecessarily. Nevertheless, the practical application of this technology is faced by several challenges including the ability to detect pests correctly especially the ones characterized as tiny and / or augmented. In potato cropping system Colorado potato beetle is an aggressive pest which should be sprayed when the beetle is in the larvae stage. Trying to detect larvae using a camera mounted on a sprayer may seem a logical method to detect larvae but its tiny size in comparison to leaves and the existence of other tiny objects in the image background makes it a difficult object to detect. In this project, using a set of cameras mounted on a sprayer, more images will be taken to annotate larvae in preparation to building an image processing software to detect them. Meanwhile, using an existing subset of images, an initial version of the software will be developed. This work will take place at the DAL-AC campus as well as at a commercial potato field in New Brunswick. The field work will include operating machine vision system mounted on a sprayer, using software to annotate objects, and helping in writing software to detect them. Students interested in this project will work with a team of graduate students at the Department of Engineering. The requirements for this project are basic knowledge of computer programming and electric circuits.

Project title: Smart control for autonomous spraying based on Controller Area Network

Real-time autonomous spraying based on sensor is an interesting application of precision agriculture that are faced by many challenges to be realized. One of them is the mechanical constraints due to the slow response of mechanical components in comparison to the speed of data flow. In this research, a smart controller that can pro-actively regulate flow of pesticides in pipes in response to data generated by machine vision is being developed. The flow of data is based on a communication protocol called Controller Area Network found in all modern automobiles characterized by the virtual lack of communication errors. In the project, a control unit that consists of microcontrollers will be developed and tested in lab and field. Also, the design of pumping system of pesticides will be investigated. . This work will take place at the DAL-AC campus as well as at a commercial potato field in New Brunswick. The field work will include learning the basics of machine communication, helping in testing the controller, data arrangement and illustration, and possible field work mounting hardware on actual sprayer. Students interested in this project will work with a team of graduate students at the Department of Engineering. The requirements for this project are basic knowledge of computer programming, the basics of electric circuits, and Engineering drawing using CAD or other software.

Dr. Yunfei Jiang

Department of Plant, Food, and Environmental Sciences

yunfei.jiang@dal.ca

Project 1: Pea-brassica intercrops

The objective of this project is to explore underlying mechanisms driving outyielding potential in pea-brassica intercrops compared to sole crops, as well as to evaluate the carryover effects of pea-brassica intercrops on the subsequent carrots/potatoes. Field trials will be conducted at the Plumdale Field Station on College Road, Bible Hill, NS during the growing season of 2025.

Project 2: Effect of enhanced efficiency fertilizer on soil microbiology

There are potential agronomic and environmental benefits of enhanced efficiency fertilizers (EEF), such as reducing N loss and greenhouse gas emission as well as improving crop yield. However, limited information is available regarding the effect of the coating materials and inhibitors of these EEF products on soil microbiology. This proposed project will fill in the gap by studying the effect of EEF products on soil microbiology. This is a lab-based project and will be co-supervised by Dr. Rhea Lumactud and Dr. Yunfei Jiang.

Project 3: Winter canola and winter camelina

The objective of this project is to evaluate key agronomic aspects (including seeding dates, seeding rates, and cultivars) on the growth performance, seed yield, and seed quality of winter oilseed crops (i.e., winter canola and winter camelina). Winter canola and winter camelina plots were established at the Boulden Field on College Road, Bible Hill, NS in fall 2024.

Project 4: Right place for corn nutrient management

Building on previous experiments conducted in our group, this project is to further study one aspect (focusing on right place) of the 5R (right source, right rate, right time, right place, and right rotation) nutrient management. Three different placement methods will be used, including broadcast, side dress, and liquid fertilizer spraying at V6 growth stage of corn, to compare the agronomic and environmental performance.

Project 5: Abiotic stress during plant reproduction

The project is to evaluate the effect of drought/heat stress during reproduction on seed yield and seed quality (such as fatty acid profile, oil, protein, and glucosinolates) in the oilseed crop, camelina. The project will be conducted in greenhouse/growth chamber and the laboratory.

Dr. Rhea Amor Lumactud
Dept. of Plant, Food, and Environmental Sciences
rheaamor@dal.ca

This research builds on last year’s NSERC-USRA project, which explored the potential of bacterial inoculant communities to enhance plant resilience under drought stress. This year, we focus on how these bacterial inoculants influence nematode communities, examining their abundance, diversity, and functional roles immediately after drought and following a one-month recovery period. Nematodes play a vital role in nutrient cycling and significantly influence microbial activity by grazing on bacteria and fungi. This grazing promotes nutrient turnover and enhances microbial diversity, making nematodes critical for maintaining soil health. This project aims to investigate the interactions between bacterial inoculants and nematodes, as well as how these nematodes contribute to microbial stability and ecosystem recovery during drought conditions.

Dr. Suresh Neethirajan
Dept. Animal Science and Aquaculture
sureshraja@dal.ca

Project 1 - Enhancing Laying Hen Welfare Through Multimodal Data Collection and Analysis

This project is focused on the exciting intersection of animal welfare and advanced technology. The overall goal is improving the well-being of laying hens in free-run housing systems by collecting and analyzing multimodal data, including video, audio, and thermal imaging, alongside environmental metrics. By actively engaging with cutting-edge tools and methodologies, the research student will gain hands-on experience while contributing to meaningful advancements in poultry welfare.

The project begins with an introduction to key welfare indicators and ethical considerations for data collection. Students will set up cameras, microphones, and thermal imaging devices in the first three weeks, preparing for real-time monitoring. Over the next four weeks, they will collect data on hen behavior, vocalizations, and thermal patterns, while also recording environmental parameters such as temperature and humidity. Midway through, students with the help of postdoctoral fellow and graduate students as a team will preprocess and annotate the collected data, identifying behaviors, vocal patterns, and temperature changes. Weeks 11 to 14 focus on analysis, where student along with the mooanalytica research group members will use artificial intelligence and machine learning tools to uncover patterns and correlations between welfare indicators and environmental factors. The project combines scientific rigour with practical application, equipping the research intern to tackle real world challenges in animal welfare using innovative technologies. 

Project 2 - Unveiling Social Dynamics in Dairy Cows Through Video-Based Social Network Analysis

This project offers undergraduate students an opportunity to explore the intricate social behaviors of dairy cows through the lens of social network analysis. By capturing and analyzing video data on cow movements and interactions, students will gain valuable insights into herd dynamics and their implications for welfare, resource access, and management.

The project begins with an introduction to social behaviors in dairy cows and the fundamentals of social network analysis, providing students with the foundational knowledge needed for the study. Over the first three weeks, students will set up video cameras in dairy cow housing facilities to capture movement patterns and social interactions. During weeks 4 to 7, they will collect video footage focusing on behaviors such as proximity, aggression, and affiliative interactions. In weeks 8 to 10, the research intern USRA student will work with graduate students and research team members of mooanalytica research group to annotate the collected data, identifying key interactions and mapping cow movement paths. The final phase of the project (weeks 11 to 14) will involve using social network analysis software to identify herd leaders, assess group cohesion, and analyze the impact of social structures on cow welfare and productivity. This hands-on project equips students with practical experience in data collection and analysis while fostering critical thinking about how social behavior influences dairy cow welfare and management strategies.