AI and the Potato Industry
Artificial Intelligence for the Potato Industry — Growers, Industry & Partners
Artificial Intelligence for the Potato Industry — Growers, Industry & Partners
Hands-on practice scenarios to help you understand how to use AI tools effectively in your operation
These exercises provide real-world scenarios you might encounter in your potato growing operation. Each exercise includes:
Remember: Always verify AI recommendations with your own expertise and start with low-risk applications.
Time: ~10 min · Level: Starter
Historical yield, planting/harvest dates, soil pH, organic matter, and weather-related columns (2020–2024). Use as the “attached spreadsheet” in your prompt.
Sample (first row of the file):
| year | variety | field_id | yield_tons_per_ha | planting_date | harvest_date | soil_pH | organic_matter_pct | avg_growing_temp_c | rainfall_mm | notes |
|---|---|---|---|---|---|---|---|---|---|---|
| 2020 | Russet Burbank | F02 | 32.2 | 2020-05-24 | 2020-09-02 | 5.3 | 2.0 | 20.7 | 152 | — |
Column headers: year – season year; variety – potato variety (e.g. Russet Burbank); field_id – field identifier; yield_tons_per_ha – yield in tons per hectare; planting_date and harvest_date – key dates; soil_pH and organic_matter_pct – soil test values; avg_growing_temp_c and rainfall_mm – growing-season weather; notes – optional comments (e.g. ideal conditions, dry spell).
You want to predict this year's yield for your Russet Burbank potatoes. You have historical yield data from the past 5 years, current soil test results, weather data, and planting dates.
"Based on my yield data from 2020-2024 (attached spreadsheet), current soil test results showing pH 5.8 and 2.5% organic matter, and the 14-day weather forecast predicting average temperatures of 18°C with 120mm rainfall, predict my expected yield for Russet Burbank potatoes planted on May 15th. Provide a range (low, medium, high) and explain the key factors affecting the prediction."
Time: ~10 min · Level: Starter
Soil moisture, irrigation and rainfall amounts, and temperatures over a multi-week period. Use with your AI prompt to build an optimized schedule.
Sample (first row of the file):
| date | soil_moisture_pct | irrigation_mm | rainfall_mm | temp_avg_c | field_id |
|---|---|---|---|---|---|
| 2024-07-01 | 61.4 | 21 | 0 | 23.5 | F1 |
Column headers: date – record date; soil_moisture_pct – soil moisture as % of capacity; irrigation_mm – irrigation applied (mm); rainfall_mm – rainfall (mm); temp_avg_c – average temperature (°C); field_id – field identifier.
You want to optimize your irrigation schedule to reduce water usage while maintaining crop quality. You have soil moisture sensor data, weather forecasts, and historical irrigation records.
"Create an optimized 2-week irrigation schedule for my potato fields. Current soil moisture is at 65% capacity. The 14-day forecast shows 3 days of rain (15mm total) and average temperatures of 20°C. My historical records show I typically irrigate every 3 days with 25mm per application. Recommend a schedule that maintains soil moisture between 60-80% capacity while minimizing water usage. Explain the reasoning for each irrigation event."
Time: ~10 min · Level: Intermediate
Historical potato prices by date, variety, and region (2020–2024). Use as “attached data” when asking for selling-window recommendations.
Sample (first row of the file):
| date | price_per_ton_cad | variety | region |
|---|---|---|---|
| 2020-01-08 | 392 | Umatilla Russet | Taber |
Column headers: date – price observation date; price_per_ton_cad – price in Canadian dollars per ton; variety – potato variety; region – market or region (e.g. Southern Alberta, Lethbridge area).
You're deciding when to sell your stored potatoes. You have historical price data, current market prices, storage costs, and information about your crop quality and quantity.
"Analyze my historical potato prices from 2020-2024 (attached data) and current market conditions. I have 500 tons of stored Russet Burbank potatoes. Storage costs are $2/ton/month. Current market price is $450/ton. Recommend the optimal selling window over the next 4 months, considering price trends, storage costs, and quality degradation risk. Provide a risk assessment for each option."
Time: ~10 min · Level: Intermediate
Sample field notes: weather, irrigation, growth stage, and symptom description. Use with (or without) photos when prompting for disease ID. Always confirm with an agronomist.
Sample (excerpt):
FIELD & CROP: - Variety: Russet Burbank - Current growth stage: Flowering (approx. 8 weeks from planting) - Field: East side | Soil type: Sandy loam RECENT WEATHER: Avg temp 22°C, humidity 75%, rainfall 29 mm IRRIGATION: Every 2 days, 26 mm per event OBSERVATIONS: Brown spots with yellow halos on leaves (mid to lower canopy)
What the file contains: Structured notes in sections: FIELD & CROP (variety, planting date, growth stage, field, soil type); RECENT WEATHER (temps, humidity, rainfall, leaf wetness); IRRIGATION (schedule, application amount, last irrigation); OBSERVATIONS (symptoms, e.g. leaf spots, stem lesions). Paste this (and optionally attach photos) into your prompt for disease ID and treatment advice.
You've noticed some unusual spots on your potato leaves. You have photos of the affected plants, information about recent weather conditions, and your crop management records.
"I've attached photos of potato leaves showing brown spots with yellow halos. The plants are in the flowering stage, planted 8 weeks ago. Recent weather has been humid (75% average) with temperatures 18-22°C. I've been irrigating every 3 days. Identify the likely disease, explain why, recommend treatment options with cost estimates, and suggest prevention strategies for next season."
Time: ~10 min · Level: Intermediate
Expense records by category and year (2022–2024). Use as “attached” when asking for budget analysis and cost-saving opportunities.
Sample (first row of the file):
| year | category | amount_cad | notes |
|---|---|---|---|
| 2022 | Seed | 55005 | As planned |
Column headers: year – season or fiscal year; category – expense category (e.g. Seed, Fertilizer, Fuel, Chemicals, Labour, Equipment repair, Irrigation, Storage, Transport); amount_cad – amount in Canadian dollars; notes – optional (e.g. As planned, Over budget).
You're planning next season's budget and want to optimize costs while maintaining quality. You have expense records from previous years, current input prices, and yield projections.
"Create a budget analysis for next season's potato crop. I have expense records from 2022-2024 (attached). Current fertilizer prices are up 15%, seed costs are stable, and fuel is up 8%. Target yield is 35 tons/hectare. Identify the top 3 cost-saving opportunities without compromising yield or quality. Provide a budget breakdown by category and compare to last year's actual costs."
Time: ~10 min · Level: Starter
Based on questions from registrants: data input, synthesizing information, and using AI for admin and reporting.
Field_Scouting_Meeting_Notes.txt
Synthetic field scouting and meeting notes (Faker-generated). Use as “sample field notes” to practice turning raw notes into a short summary and traceable report format. You can also use your own notes or Disease_Scenario_Notes.txt.
Sample (excerpt):
Date: Tuesday, February 24, 2026 Field: F29 – North block Scout: Judith RAW NOTES: No pest issues; soil moisture in North block lower; irrigation head at row 12 leaking – maintenance called; flowering nearly complete; tuber set looks even. Actions: Follow up on fungicide timing; review irrigation with John; email agronomist re possible blight. Meeting: Discussed harvest window – no decision yet; next site visit in five days.
What the file contains: Unstructured field and meeting notes: Date, Field, Scout (who and where); RAW NOTES – bullet points from scouting (pests, soil moisture, irrigation issues, crop stage); Actions / follow-up – rough to-do items; Meeting / call notes – decisions or next steps. Use this to practice having AI summarize into a short report and suggest a structure for future traceable notes.
You have rough notes from a field scouting visit or a meeting. You want a short, professional summary with clear action items and priorities—suitable for sharing with your agronomist or partner. You also want to see how AI can help you “input your data” so it can be analyzed or reported later.
"Summarize this field scouting report into a short summary (3–5 bullet points) with any recommended actions and priorities. Keep the tone professional and suitable for sharing with my agronomist or partner. Then suggest a simple structure (headings and key fields) I could use to turn future notes into traceable, report-ready format."
Time: ~10 min · Level: Intermediate
Based on questions from registrants: disease diagnostics, optical sorting, storage tools, and “searching for solutions” with AI.
Machine_Vision_Search_Scenarios.txt
Sample scenarios (Faker-generated operation names and regions). Pick one and paste into your prompt, or use your own. Use AI to search and synthesize: find what’s available for disease ID from images, storage tools, or optical sorting and compare options.
Sample (one scenario from the file):
--- Scenario 1 --- Operation: Doyle Ltd Farms Region: Southern Alberta Interest: I want a short overview of how AI and machine vision are used for plant disease identification from leaf images.
What the file contains: Several short scenarios, each with Operation (grower or business name), Region (e.g. Southern Alberta, Lethbridge area, Taber), and Interest – one of: disease ID from leaf images, potato storage management, or optical sorting in packing. Pick one scenario (or substitute your own) and paste it into your AI prompt when asking for an overview of tools and what to consider.
You’re interested in using AI for plant disease diagnostics (e.g. from leaf images) or in understanding what AI tools exist for potato storage or optical sorting. You want a concise overview of what’s out there, how it works in plain language, and what to consider before adopting.
"I'm a potato grower in Southern Alberta. Give me a short overview of how AI and machine vision are used for (choose one: plant disease identification from leaf images / potato storage management / optical sorting in packing). Include: what the technology does in plain language, 2–3 examples or product types if you know them, and what I should consider (cost, data, accuracy) before trying it. Keep it practical for a grower or operations manager."
Apply these checks to every AI recommendation:
Now that you've reviewed these exercises, consider: