🌱 Day 6 of 365 — AgriTech AI Journey | A Farmer Can Now Use This Without Knowing What Code Is
Six days ago I set up a Python environment.
Today I have a working web application that a farmer with a basic smartphone can open, fill in their farm details, and receive an AI-generated yield prediction, income estimate, risk assessment, and monthly farming calendar — all in under two seconds.
No terminal. No code. No technical knowledge required.
Here is what the app does when a farmer submits their details:
The green banner shows their predicted yield in tons, alongside low, mid, and high income estimates in Naira based on current market price ranges for that crop.
The risk flags section catches problems before the season starts. When I tested it with Alhaji Musa's profile — a maize farmer in Kano with no irrigation, low rainfall, and traditional seeds — the system flagged three risks immediately. When I tested Mrs Adunola's cassava farm in Oyo with improved seeds and good rainfall, the risk section showed: "No risk flags — your farm setup looks good." That contrast tells the whole story.
The what-if section shows the farmer exactly what their yield could be if they upgraded their inputs — improved seeds, irrigation, and 100 kg/ha fertilizer. For Alhaji Musa, the potential uplift was over 50%. That number alone could change a farmer's investment decision for the season.
The crop calendar shows three months in view — what should have happened last month, what to do this month (highlighted in amber), and what to prepare for next month.
The technical stack is clean: → HTML/CSS/JS frontend (mobile-first, works on any browser) → Nigerian green colour theme → Real-time server health badge (AI Online / Server Offline) → Form validation before the API is ever called → Parallel API calls for prediction and calendar data simultaneously → Flask REST API (Day 5) serving all the data → scikit-learn Random Forest model (Day 3) doing the prediction
Six days. Six layers of the same product stacked on top of each other.
Day 1 — data Day 2 — real data from the internet Day 3 — machine learning model Day 4 — farmer advisory tool Day 5 — REST API Day 6 — web interface any farmer can use
Tomorrow I start connecting this to a public server so it is no longer localhost.
#AgriTech #Day6of365 #BuildInPublic #Nigeria #AI #WebDevelopment #Flask #Python #SmallholderFarming #AfricanInnovation #FUNAAB #MachineLearning #HTML #CSS #JavaScript
26 Septiembre 2026 à 19h21
Ver en LinkedIn →
