Digital Agriculture, at its best, builds upon decades of discipline research with some integration of new IoT sensors and communication pathways as well as public resource data such as weather, soil, and topography. One challenge to be addressed is to more fully document the backstory or fuller context of situations so that artificial intelligence and machine learning can be more complete and robust. Another is the integration of mechanistic (descriptive of the fundamental science) models that might be biological, physical, chemical, logistical, economic, etc. in origin. The better parameterization of these models and even auto-population of initial conditions can stem from data sets and data streams. In this project, the student will extract biophysical model(s) from literature and other simulations to meld model + data. It will require interoperability focus and that involves wise choices of data architecture and an integration with data pipelines (often based on open source tools). The end game is to provide better insight (including probabilities, when applicable) for tactical and strategic cropping decisions while preserving security and privacy.

Internet of Things, IoT for Precision Agriculture

• No Major Restriction

Knowledge of cropping systems and coding desired.

Agricultural and Biological Engineering

Dennis Buckmaster

The goal of this project is to design an IoT4Ag sensor deployment system for autonomous agricultural ground robot. Two types of IoT sensors must be deployed by the robotic platform. Chaff sensors need to be distributed on the surface of soil at locations with designated spacing to ensure appropriate spatial coverage for the field of interest. The second type of sensors similarly need to be spread about the field but require them to be inserted into the soil at a depth of approximately 3” deep. Thus, the developed sensor deployment system should be able to 1. Store the sensors that need to be deployed; 2. Distribute sensors at a designated spacing above the soil; and 3. Insert the sensors into the ground at a designated spacing in the soil; and 4. Log the type of sensor that has been distributed, its sensor ID, and its placement location. This project will require the mechanical design of the deployment systems, mechatronic system design for operating and controlling the systems, and integration and interfacing with the agricultural ground robot for execution and tracking of sensor deployment locations. Field tests will be conducted at the Purdue University Agronomy Center for Research and Education (ACRE) facility.

Fabrication and Robotics, IoT for Precision Agriculture

• Mechanical Engineering
• Electrical Engineering
• Computer Engineering

US citizens/permanent residents only Mechanical design, mechatronics, 3D printing, electronics, robotics, programming experience preferred.

Mechanical Engineering

David Cappelleri

Infectious diseases are a leading cause of economic burden on food production from animals. For example, bovine respiratory disease leads to a loss of ~$1 billion annually. Current methods for tackling these diseases includes the administration of antibiotics by trial-and-error. This approach leads to failure of treatment in up to one-third of the cases. In addition, it also leads to a proliferation of antibiotic resistance in pathogens.
Our research project focuses on developing a low-cost user-friendly biosensor based on paper that can detect which pathogen is causing the disease and whether it exhibits antibiotic resistance. Such a biosensor would provide a readout to the farmer or the veterinary physician and suggest which antibiotics are likely to be successful.
Lab members working in the team have three objectives: i) design, test, and optimize primers for detecting pathogens and genes associated with bovine respiratory diseases, ii) build and field-test a paper-based device for conducting loop-mediated isothermal amplification, and iii) build and field-test a heating/imaging device for conducting the paper-based assay in the field.
The SURF student will work on one of the objectives depending on their background and experience.

Biological Simulation and Technology, IoT for Precision Agriculture

• Biochemistry
• Biological Engineering - multiple concentrations
• Biomedical Engineering
• Agricultural Engineering
• Mechanical Engineering
• Electrical Engineering

Relevant skills for the project: • Wet lab skills and experience with molecular biology • Autodesk Fusion 360 for 3D Modeling/Printing and Laser Cutting • Python Programming Language for image processing and graphical user-interface using Raspberry Pi (or any other single board computer) To be successful at this position, you should have a GPA>3.5, prior experience working in a lab, and the ability to work in a team.

Agricultural and Biological Engineering

Mohit Verma