Open Data ne AI: Ɔkwan a Ghana Akuafoɔ Betumi Agyina So

2025 mu, Ghana mu akuafoɔ pii reyɛ adwuma den de ahwehwɛ sika a ɛbɛtumi atɔ aduro, aba pa, ne afiri—nanso bere koro no ara, wɔn mu bebree renya nsɛm a ɛyɛ pɛpɛɛpɛ a ɛbɛma wɔn ani agye wɔ bere a wɔrekyekyere wɔn adwuma ho gyinae. Wɔtumi hunu nsuo a ɛbɛtɔ, nanso ɛhe na ɛbɛtɔ, bere bɛn na ɛbɛtɔ, ne sɛ ɛbɛyɛ dɛn na ɛbɛka wɔn adwuma no, ɛda so yɛ den.

Ɛha na open data ne AI (akomam adwumadie) bɛtumi aboa. Sɛ data no da hɔ a, AI tumi kyerɛ nea ɛrekɔ so, nea ɛrebɛba, ne nea ɛfata sɛ wokɔyɛ seesei. Na sɛ data no nnim (anaasɛ ɛda laptop mu wɔ ofis bi mu) a, AI no bɛyɛ te sɛ kar a ɛwɔ petrol nanso ɛnni kwan.

IITA (International Institute of Tropical Agriculture) yɛɛ asɛm bi a ɛma yɛn hu ade pa: IITA Open Data Challenge 2018. Na ɛnyɛ “competition” kwa. Na ɛyɛ nhyehyɛe a ɛreka kyerɛ yɛn sɛ sɛ yɛpɛ AI a ɛbɛboa akuafoɔ, ɛsɛ sɛ yɛde data pa to gua.

IITA Open Data Challenge 2018: Adwene a ɛyɛ kyɛfa ma Ghana

Asɛm no mu ntini ne sɛ: IITA hunuu sɛ data bebree fi 1990s kosi 2018 da so da “file drawer” mu—wɔ mmɔdenmu folder, laptop, hard drive, anaa notebook mu—na ɛnnya kwan nkɔ repository.

IITA de sika ne adwumakuo sii hɔ sɛ wɔnboa wɔn scientists ne wɔn hubs ma wɔmfa data no nkɔ repository (CKAN) so. Wɔde “data sprint” kwan so yɛɛ adwuma no ntɛm ntɛm.

Snippet-worthy: AI a ɛwɔ data a ɛnni quality no yɛ dɛn? Ɛyɛ “smart” a ɛma gyinae bɔne kɛse.

Goal a ɛyɛ pɛpɛɛpɛ

IITA sii botae pɛpɛɛpɛ: datasets 100, a wɔayɛ quality check, data ne metadata nyinaa a wɔakyerɛw mu yie (a ontology ka ho), na wɔde akɔ CKAN so ansaa 2018-09-30.

Botae a ɛte saa no yɛ ade a Ghana mu adwumakuo (universities, MoFA projects, agritech startups, cooperatives) betumi asua: botae a ɛwɔ nɔma, deadline, ne quality criteria.

Adɛn nti na open data ho mmɔdenbɔ yi ho hia ma AI a ɛbɛboa akuafoɔ wɔ Ghana?

Ɔkwan a AI fa so boa akuafoɔ no nyinaa fi data so. Sɛ data no yɛ “open” (anaasɛ ɛyɛ accessible wɔ nhyehyɛe pa mu), na ɛwɔ metadata a ɛkyerɛ sɛ data no kɔ he, wɔboaboa ano dɛn, bere bɛn mu, units a wɔde di dwuma, a—ɛno na ɛma AI tumi yɛ adwuma pa.

1) AI needs “context”, na metadata na ɛma context

Dataset bi betumi aka sɛ “maize yield = 3.2”. Nanso:

• 3.2 dɛn? tonnes/ha anaa bags/acre?
• ɛwɔ he? District anaa GPS point?
• bere bɛn mu? 2014 anaa 2024?
• ɔkwan bɛn so na wɔsusuu? field cut method anaa farmer recall?

Sɛ metadata nni hɔ a, AI bɛka asɛm bɔne. Sɛ metadata pa wɔ hɔ a, AI tumi ma “advice” a ɛdi mu.

2) Open data ma “shared learning” kɔ anim

Ghana mu, akuafoɔ wɔ Northern Region betumi anya asɛm firi Bono East anaa Volta mu a ɛbɛboa wɔn. Sɛ data da hɔ a, models betumi sua “patterns” wɔ agro-ecological zones mu, na ɛma advisory systems (SMS, WhatsApp, call center) yɛ adwuma yiye.

3) Open data bɔ mu ma agritech startups

Startups a wɔyɛ:

• weather advisory
• pest/disease detection
• input recommendation
• market price forecasting

…wɔn nyinaa hia data. Sɛ data no yɛ open (anaasɛ wɔde licensing ne governance hyɛ mu yie) a, ɛtew wɔn cost, na ɛma wɔn tumi si solutions ma akuafoɔ ntɛm.

“Data Sprint” kwan: Adeɛ 7 a Ghana mu adwumakuo betumi afa so

IITA’s process no yɛ practical. Sɛ wopɛ sɛ wode AI boa akuafoɔ, ɛnyɛ sɛ wokɔtɔ software kwa—yɛ data hygiene ansa.

1) Fa “submission pipeline” to nsa

IITA ma researchers soma datasets kɔ CKAN officer. Ghana mu, wobɛtumi ayɛ saa ara:

• “data inbox” (email/portal)
• template a ɛma submission yɛ easy
• checklist (files, formats, consent)

2) Metadata form a ɛyɛ standard

IITA yɛ adwuma wɔ CG Core metadata schema so. Ghana mu, point no ne sɛ: yɛ standard bi na fa di dwuma daa.

Minimum metadata a ɛfata:

• dataset title, owner, contact
• location coverage
• time period
• variables + units
• data collection method
• quality notes
• licensing/usage terms

3) Curate data ansa na wobɛupload

Curation kyerɛ:

• removing duplicates
• fixing missing values (anaasɛ marking them clearly)
• consistent naming conventions
• data dictionary

4) Upload to a repository a ɛtumi “serve” data

IITA de CKAN di dwuma. Ghana mu, wobɛtumi ayɛ CKAN-like setup anaa repository a ɛma:

• search
• versioning
• access control
• APIs

5) Monitor performance, na yɛ ranking

IITA monitor data sprint performance na wɔyɛ ranking. Point no ne sɛ: what gets measured gets done.

6) Incentives a ɛma nnipa de wɔn data to gua

IITA de incentives (conference funding) maa winners. Ghana mu, incentives betumi ayɛ:

• research recognition
• small grants
• equipment support (tablets, GPS devices)
• publication support

7) Quality first, not quantity

IITA kaa “quality-checked” wɔ wɔn goal mu. Me stance no: dataset 30 a ɛyɛ clean na ɛwɔ metadata pa bɛboa AI sen dataset 300 a ɛyɛ messy.

Sɛ data no bɛyɛ open a, akuafoɔ benya mfasoɔ bɛn pɛpɛɛpɛ?

Answer first: Open data + AI = better farming decisions at lower cost.

1) Weather advisory a ɛyɛ local

Ghana mu, rainfall pattern yɛ “micro”. AI tumi fa:

• historical rainfall
• soil moisture proxy
• planting dates
• yield outcomes

…na ɛma advice te sɛ: “Plant maize within 7 days after first effective rain” anaa “Delay fertilizer top-dress by one week because heavy rain likely.”

2) Pest and disease early warning

Fall armyworm, cassava mosaic, cocoa swollen shoot—wɔyɛ dɛn na wobɛdi kan ahu? Sɛ data wɔ hɔ (incidence, location, seasonality), AI tumi:

• predict hotspots
• guide scouting routes
• reduce pesticide misuse

3) Input recommendation a ɛtew sika

AI tumi fa yield response data ne soil data kyerɛ:

• fertilizer rate a ɛyɛ cost-effective
• variety selection for zone
• spacing + planting density

4) Market insights a ɛma bargaining yɛ den

Open price datasets (market-level) ma AI tumi:

• forecast seasonal price dips
• recommend selling windows
• compare markets within region

One-liner: Data ma akuafoɔ tumi di dwa; AI ma data no kasa.

“People also ask” style nsɛmmisa: Nea Ghana mu nnipa taa bisa

AI bɛtumi adi dwuma a data no sua a?

Ɛtumi, nanso ɛyɛ den. Sɛ data sua a, model no bɛyɛ biased, na advice no bɛyɛ general. The win no ne sɛ: start small, but standardize. Dataset 10 a ɛyɛ clean betumi sɛe mmere a ɛwɔ before dataset 100 a ɛnni metadata.

Sɛ data yɛ open a, ɛnyɛ risk?

Ɛwɔ risk. Nea ɛyɛ adwuma pa ne:

• remove personal identifiers
• aggregate sensitive locations (e.g., farm household)
• define licensing terms clearly
• keep some datasets “restricted” if needed

Hwan na ɛsɛ sɛ ɔdi kan wɔ Ghana mu?

Me adwene: research institutions + MoFA + commodity boards + agritech associations. Sɛ wɔn nyinaa di dwuma wɔ standards ne repository so a, startups ne advisory services benya foundation.

Practical plan: Ghana “Open Data Sprint” a ɛbɛboa AI wɔ 2026 planting season

Answer first: Sɛ wopɛ impact ntɛm, yɛ sprint a ɛwɔ 90–120 days, focus on a few high-value datasets.

Suggested sprint design (simple but serious):

1. Pick 3 crops: maize, rice, cassava (anaasɛ cocoa for cash crop track)
2. Pick 5 dataset types: rainfall + planting dates, yield trials, pest incidence, soil tests, market prices
3. Set a goal: e.g., 60 datasets quality-checked within 4 months
4. Train 2 roles: data stewards + metadata reviewers
5. Publish weekly scoreboard: submissions, accepted, returned for fixes
6. Ship an outcome: one pilot AI advisory tool (even if basic) to prove value

This fits the broader theme of the “Sɛnea AI Reboa Adwumadie ne Dwumadie Wɔ Ghana” series: AI doesn’t start with algorithms; it starts with workflows that make good information cheap and usable.

Nea wubetumi ayɛ seesei (sɛ woyɛ agribusiness, NGO, anaa research team)

• Inventory your data: kɔ wo office folders mu na bɔ list of datasets (even old ones)
• Create a data dictionary: variables + units + collection method
• Agree on a minimum metadata standard: na fa di dwuma daa
• Choose a repository approach: internal first, then public datasets later
• Build one “farmer-facing” output: SMS tips, WhatsApp audio, or extension dashboard

Ɛnyɛ sɛ wobɛyɛ biribi kɛse seesei. Nanso yɛ biribi a ɛbɛtumi akɔ so.

Nea IITA Open Data Challenge kyerɛ yɛn pɛpɛɛpɛ

IITA Open Data Challenge 2018 kyerɛɛ sɛ data ho adwuma yɛ management project, ɛnyɛ “side task”. Wɔde botae, process, quality checks, monitoring, ne incentives hyɛɛ mu. That’s why it worked.

Sɛ Ghana pɛ sɛ AI bɛboa akuafoɔ—ma wɔnnya better yields, tew input waste, na wɔnnya market power—open data initiatives yɛ foundation a ɛwɔ hɔ ma biribiara.

Wobɛyɛ dɛn de wo institution’s data bɛyɛ “AI-ready” wɔ afe a ɛreba mu? Na wobɛyɛ dɛn ama akuafoɔ no ankasa anya mfasoɔ a ɛte sɛ advice a wotumi de di dwuma nnɛ, ɛnyɛ report a ɛda shelf so?