Data Science Applications in Agriculture
Beasts & Bytes
Course description and aims
This course offers an undergraduate experience in challenge-based learning, where students tackle data-driven problems within the agricultural sector. If you have never heard about it, check out this Solar Car Challenge that you might recognize. Working in collaborative teams, students will engage directly with farmers and industry stakeholders to understand a real-world challenge and develop an innovative, data-driven solutions.
Throughout the course, students will learn to utilize a range of digital tools essential for modern data science. Key topics covered include data governance, ensuring data privacy, effective data collection methods, efficient data storage solutions, data processing techniques, and advanced data visualization. Students will also delve into data modeling and the art of presenting data in a clear and impactful manner.
Project management is a core component of the course, with students learning to manage projects effectively, adhere to deadlines, and deliver results in a timely manner. The course emphasizes the importance of teamwork, communication, and collaboration, preparing students to work in diverse and dynamic environments.
By the end of the course, students will have gained practical experience and developed a skill set that includes problem-solving, critical thinking, and the ability to create sustainable solutions for the agricultural industry. This course is ideal for students interested in applying data science to real-world problems and making a tangible impact in the field of agriculture.
Credits
This course accounts for 3 credits
Prerequisites
A statistical course such one of the following is not required but strongly advised:
STSCI 2150/5150 Introductory Statistics for Biology
BTRY3010/STSCI2200 & BTRY5010/STSCI5200: Biological Statistics I
ILRST/STSCI 2100: Introductory Statistics
Learning outcomes
Explain the fundamentals of data science and digital agriculture including their interdisciplinary challenges.
Implement the various data science technologies, precision farming techniques and digital tools used in modern agriculture.
Conduct data collection, visualization, analysis, and interpretation within the context of digital agriculture.
Acquire practical skills through hands-on programming activities.
Explore the applications of machine learning and artificial intelligence in agriculture.
Recognize the significance of, and apply data privacy and security techniques in digital agriculture.
Outline
See this link for details on course outline. As this course is under constant construction, expect updates to happen to this course outline during the spring semester of 2026.
Format
This course will consist of a combination of lectures, hands-on practical sessions, a case study, group discussions, and online programming exercises. Students will have the opportunity to work on a real-world project related to digital agriculture.
General week format
| Monday | Tuesday | Wednesday | Thursday | Friday | |
|---|---|---|---|---|---|
| 10:10-11:00 am | Lecture | Discussion | |||
| 1:25-4:25 pm | Lab |
Lectures
The goal of the lectures is to have them as interactive as possible (which requires your attendance and participation). My role as instructor is to introduce you new tools and techniques, but it is up to you to take them and make use of them.
Labs
One the things you will definitely learn in this course will involve writing code, and coding is a skill that is best learned by doing. Therefore, as much as possible, you will need to finish a weekly coding assignment. Every week a new assignment will be opened for each of you on the online coding platform Dodona. There is a strict deadline at the day before the next lab (monday at 12 pm) to finish the assignment, individually. There will not be a deadline during the february and spring break. The number of successfully finished assignments at the end of the course is part of your individual grading (see below). You are allowed to use AI for these exercises, but it is strongly recommended to not use it to much as you will not be allowed to use it at the programming exam (see below). Try to ask your class mates first when you have troubles finishing an exercise or go to the office hours of this course. Dodona recommends using the programm Pycharm, however we will be using Google Colab for the programming exercises. Every student will get an individual folder in the shared Beast & Bytes drive and can use this to store their notebooks.
Weekly project meetings
We will hold weekly project meetings during which we will discuss the groups overall progress, address any challenges, and provide guidance. These meetings are an opportunity for you to share updates, receive feedback, and collaborate with your peers and instructors. Your active participation is crucial to ensure that you stay on track and achieve the project goals.
Project
Throughout the course, you will engage in a group project that involves solving a real-world, data-intensive problem in agriculture. Working in teams, you will collaborate with farmers and stakeholders to identify the exact problem. Your task will be to develop a data-driven solution using digital tools and techniques learned during the course.
Differrent project phases we will tackle together
Problem Identification: We will begin by meeting with the farmer (and other stakeholders involved) to understand the problem and their needs. This will include conducting an initial research to better define and scope the problem clearly.
Data Collection: You will gather relevant data from various sources, ensuring adherence to data privacy and governance standards.
Data Analysis and Visualization: You will analyze the collected data (using techniques covered in the labs) to describe insights and trends, including the use of visualization tools to present your findings effectively.
Solution Development: Next, different statistical, or machine learning techniques will be explored to develop a practical solution. This phase will involve hands-on programming.
Implementation Plan: Create a detailed plan for implementing the proposed solution.
Presentation and report: You will prepare and present your project to the farmer, including your methodology, findings, and proposed solution. This will be summarized in a final project report
Website
All lecture notes, assignment instructions, an up-to-date schedule, and other course materials may be found on the course website at bovi-analytics.github.io/BeastsAndBytes/.
Although I will try to avoid last minute changes to the schedule this might happen given this challenged based learning course. I will send course announcements via email.
Assessment & grading
Assessment methods will include individual coding assignments, a coding exam and a group project including presentations and group report.
Group assessment
Together with Dr. Hostens the group will set goals for the project. You get to decide what makes a succesfull project and grading will be based on acchieving those goals. This might sound a bit vague for now, but we will discuss this elaborately in class.
Individual assessment
It is very important to read this part as your individual assessments will be influencing your individual gradings.
Dodona assignments
At the end of the course you will be evaluated on the % of weekly assignments you have coded correctly in the Dodona framework (which you will learn during the course). That percentage will be multiplied with your grade for the programming exam, resulting in your final individual grade. The individual grade will be combined with the group grade to make the final grade.
Finishing each of the assignments within each deadline is not that difficult. You “just” need to devote the time to it. I have used this method a lot during my courses. It is not to annoy students, but to make sure the entire group keeps progressing on the programming skills during the project. You will thank me for that at a certain moment. For full transparency, I learned to code Python using a similar approach. Once I understood why this was done, I started appreciating this method as one of the most inspiring learning methods I have ever seen.
Programming exam
At the end of the course there will be an individual programming exam in the program Dodona (in class). It is not allowed to use generative AI for this exam (which will be monitored in class). The exam will consist of three programming exercises similar to the exercises covered in the course. There will be practice exams available to prepare you for this test. The date of this exam is still to be decided.
You will receive an individual score for this part.
Final individual score
The percentage of weekly correctly finished assignment will be multiplied with your programming exam, resulting in your final individual grade. Keep in mind that finishing less than 50% of your weekly assignments will result in the mathematical impossibility to pass for this component of the exam (49% * 100 < 50%).
Final grading scale
The final scale will combine your individual score (50%) and group project score (50%). We will determine jointly in one of the first lectures how the group project will be graded.
An example : - The group project was finally graded at 100% (which is definitely possible). - Student A successfully finished 100% of the coding assignments within each deadline, and finished the exam successfully with a score of 90%. As a result student A gets (50% * 100%) + (50% * 100% * 90%) = 95% as the final individual grade. - Student B only finished 50% of the coding assignments within each deadline, and finished the exam successfully with a score of 70%. As a result student B gets (50% * 100%) + (50% * 50% * 70%) = 67% as the final individual grade.
| Grade | Low | High |
|---|---|---|
| A+ | 99.80 | 100.0 |
| A | 93.33 | 99.80 |
| A- | 90.00 | 93.33 |
| B+ | 86.66 | 90.00 |
| B | 83.33 | 86.66 |
| B- | 80.00 | 83.33 |
| C+ | 76.66 | 80.00 |
| C | 73.33 | 76.66 |
| C- | 70.00 | 73.33 |
| D+ | 66.66 | 70.00 |
| D | 63.33 | 66.66 |
| D- | 60.00 | 63.36 |
| F | 0.00 | 60.00 |
Each grade range includes the score on the left, and excludes the score on the right. For example, a 90.0 is an A-, and not a B+. An 89.99 is a B+, not an A-.
Policies
Inclusive community
I grew up in a family in which values as diversity, equity and inclusion were at the core of our everyday life. My parents were both involved taking care of people struggling with equity and inclusion, and as a result these values are deeply embedded in my character.
I aim to ensure that students from all diverse backgrounds and perspectives are well-served by this course. I strive to address students’ learning needs both in and out of class, and to view the diversity that students bring as a resource, strength, and benefit. My goal is to present materials and activities that respect diversity and align with Cornell University’s core values. Sometimes it might fade during busy times, don’t be afraid to recall someone, we’re all humans after all. “Your suggestions are truely encouraged and appreciated”. Please let me know how I can improve the course’s effectiveness for you personally, or for other students or student groups.
Additionally, I aim to foster a learning environment that embraces a diversity of thoughts, perspectives, and experiences, and respects your identities. If your experiences outside of class are affecting your performance, please feel free to talk with me. Alternatively, your academic dean is a great resource if you prefer to speak with someone outside the course.
Academic Integrity
Absolute integrity is expected of every Cornell student in all academic undertakings. Integrity entails a firm adherence to a set of values, and the values most essential to an academic community are grounded on the concept of honesty with respect to the intellectual efforts of oneself and others, and free and open inquiry and discussion in the classroom. Academic integrity is expected not only in formal coursework situations, but in all University relationships and interactions connected to the educational process, including the use of University resources. While both students and faculty of Cornell assume the responsibility of maintaining and furthering these values, this document is concerned specifically with the conduct of students.
A Cornell student’s submission of work for academic credit indicates that the work is the student’s own. All outside assistance should be acknowledged, and the student’s academic position truthfully reported at all times. In addition, Cornell students have a right to expect academic integrity from each of their peers.
This a guideline for students offered through the Office of the Dean of Faculty
Responsible Use of Generative Tools
In this class, we recognize the potential of generative tools to enhance learning and creativity. However, it is crucial to use these tools responsibly and ethically. Here are some guidelines to ensure their proper use:
Academic Integrity: Always attribute the work generated by these tools appropriately. Do not present AI-generated content as your own original work without proper citation.
Critical Thinking: Use generative tools as a supplement to your own thinking and analysis. Evaluate the outputs critically and ensure they align with the academic standards and objectives of our course.
Ethical Considerations: Be mindful of the ethical implications of using generative tools. Avoid generating content that could be harmful, misleading, or inappropriate.
Privacy and Security: Respect privacy and confidentiality when using generative tools. Do not input sensitive or personal information into these tools.