Spring 2027

Artificial Intelligence in Agriculture

Explores AI technologies for optimizing agriculture, covering advanced sensors, data processing, visualization, and machine learning models for decision-making.

offering recorded3 credits
Recorded instructors · Spring 2027 Zhou Zhang

Summary

1 / 3

Recent recorded grades — Spring 2024: 3.54 GPA, 85.7% A/AB (n=14 letter grades); Spring 2025: 3.71 GPA, 90.5% A/AB (n=21 letter grades); Spring 2026: 3.74 GPA, 100.0% A/AB (n=35 letter grades).

What you’ll be able to do

  • Apply advanced sensing and computing techniques and modern engineering tools for intelligent agricultural system design

  • Apply basic machine learning models and engineering principles to solve digital agriculture problems

  • Identify important resources to retrieve, interpret, analyze, and critique information for use in solving problems in digital agriculture

  • Deliver presentations to an audience of peers discussing the development of machine learning models and interpretation of modeling results

Source: UW Guide

Grade history

↗
average GPA
letter grades
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AB
B
BC
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All recorded terms · compare terms & instructors

Prerequisites

Course map

BSE 380,COMP SCI 220, 300, or graduate/professional standing

BSE 405

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

Recent recorded grades — Spring 2023: 3.59 GPA, 81.8% A/AB (n=11 letter grades); Spring 2024: 3.54 GPA, 85.7% A/AB (n=14 letter grades); Spring 2026: 3.74 GPA, 100.0% A/AB (n=35 letter grades).

Historical instructors & teaching patterns

ZHOU ZHANG is recorded teaching in Spring 2023, Spring 2024, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 390

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Recent recorded grades — Spring 2024: 3.54 GPA, 85.7% A/AB (n=14 letter grades); Spring 2025: 3.71 GPA, 90.5% A/AB (n=21 letter grades); Spring 2026: 3.74 GPA, 100.0% A/AB (n=35 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Advanced sensors for data acquisition
  • Data processing and visualization
  • Machine learning models for agricultural decision making
  • Real-world AI implementation in agriculture

Skills

  • Advanced sensing and computing techniques for intelligent agricultural systems
  • Application of basic machine learning models to digital agriculture problems
  • Retrieving, interpreting, analyzing, and critiquing information for digital agriculture
  • Presenting machine learning model development and interpretation to peers

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records
BSE 405 · Spring 2027

Artificial Intelligence in Agriculture

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:112:026267",
    "course_id": "BSE 405",
    "course_uid": "course_8a92316192bfc8e9be89f373",
    "term_id": "1274",
    "source_course_id": "026267",
    "source_subject_id": "112",
    "title": "Artificial Intelligence in Agriculture",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews

No records available.

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "97e5302eb1d65c418a5f59878b5eeb7f303060180fa6ef94f9790df0a6a4b8d4",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}