Ag Drones & AI: What DJI’s US Crisis Teaches Ghana

About 4 out of every 5 ag spray drones used by US farmers are DJI models—that’s the estimate DJI shared in 2024. Now picture what happens when a market relies that heavily on one supplier, and regulators suddenly threaten to block new models and even review approvals for existing ones.

That’s not just an American story. It’s a loud warning for Ghana’s agriculture and for every agribusiness or cooperative thinking: “Let’s just import the tech and move on.” Most SMEs get this wrong. They buy hardware first, then later discover the real dependency is software, data access, and compliance.

This post uses the DJI situation in the US as a practical lens for our series, “Sɛnea AI Reboa Adwumakuo Ketewa (SMEs) Wɔ Ghana.” The point isn’t drones alone—it’s how AI in agriculture and automation should be designed so Ghanaian farmers and agribusiness SMEs aren’t trapped by supply chain shocks, policy changes, or “data locked inside the controller.”

What’s happening to DJI in the US—and why it matters

The key point: regulation can change the market overnight, and when it does, the winners are the people who control their data and can switch tools without losing operations.

In the US, a clause in the defense spending framework requires a national security review of certain Chinese drone makers. If the review isn’t completed by a specific deadline, DJI risks being added to a list that limits its ability to get approvals for future models—and may trigger re-evaluation of existing authorizations.

DJI has pushed back, arguing that a fact-based audit needs more time and that offers to work with agencies have gone unanswered. At the same time, DJI has faced import friction and legal setbacks tied to how the US government classifies the company.

The bigger lesson: risk isn’t only “politics”—it’s operational

Even if you don’t follow US policy debates, you should care about what this does to farmers:

• Parts availability becomes uncertain (batteries, arms, motors, controllers).
• Maintenance timelines stretch during peak season.
• Training investments lose value when models change or disappear.
• Insurance and compliance costs rise when regulators tighten standards.
• Data continuity breaks when software is tied to one device ecosystem.

For Ghana, the parallel is simple: agriculture SMEs that depend entirely on imported agtech—without local support and without data portability—carry a hidden business risk.

The DJI dilemma is really a “data ownership” problem

The clearest insight from the US spray-drone sector is this: the market is shifting from “who sells the drone” to “who controls the software and data flows.”

A major spraying services provider in the US, Rantizo, spun out a new software-focused company (American Autonomy Inc) as the sector braced for potential DJI restrictions. Their bet is straightforward: let manufacturers build aircraft, while a neutral software layer helps farmers keep control of their operational data and connect to the tools they already use.

Ghana connection: AI tools are only useful if data can move

For Ghanaian SMEs—input dealers, aggregators, spray service providers, and processors—the practical standard should be:

• Your farm data must be exportable (CSV, APIs, or at least clean reports).
• Your records must outlive the hardware (if a device fails, your history remains).
• Your tools must integrate (spraying logs, inventory, invoicing, and traceability).

If your drone, sensor, or “smart app” can’t share data with your accounting system, your cooperative records, or your buyer traceability requirements, you don’t have a system—you have a gadget.

A useful rule: If your agritech can’t produce an audit-ready report in 10 minutes, it’s not built for an SME.

Realignment creates opportunity: build locally, integrate globally

Here’s the thing about market disruptions: they create space for new players. In the US, the expectation of restrictions is encouraging manufacturers to enter the spray-drone market using more modular supply chains (controllers, motors, components) rather than building everything in-house.

Ghana can learn from this without copying it.

What “build locally” should mean for Ghana

“Local” doesn’t have to mean every circuit board is made in Accra. For Ghanaian agriculture, “local” should mean:

1. Local support and training: technicians, spare parts, and agronomy guidance.
2. Local compliance readiness: documentation for regulators, EPA rules, and buyer standards.
3. Local languages and workflows: tools that respect how farmers actually operate.
4. Local data governance: clear rules on who owns data and where it’s stored.

This aligns directly with the campaign: Sɛnea AI Reboa Aduadadie ne Akuafoɔ Wɔ Ghana—AI should support farmers while protecting their interests.

A practical stance: Ghana shouldn’t be dependent on one vendor ecosystem

If US farmers can be exposed by over-reliance on one supplier, Ghana’s risk is higher because:

• Foreign exchange pressure can delay imports.
• After-sales coverage is thinner.
• Policy shifts (telecom, aviation, pesticide regulation) can stall deployments.
• Connectivity challenges make cloud-only systems fragile.

The answer is not “avoid imported tech.” It’s to avoid imported lock-in.

How AI helps Ghanaian agriculture SMEs (beyond drones)

The main point for this series is SME impact. AI value shows up when it reduces cost, increases consistency, and improves decision-making—not when it looks fancy.

1) AI for spray services: planning, proof, and pricing

If you run or manage a spraying team, AI can help you operate like a disciplined logistics business.

What to implement:

• Job scheduling and routing: cluster farms by location and urgency.
• Mixing and inventory tracking: reduce wastage and stockouts.
• Application proof: time, location, field size, and chemical usage reports.
• Simple pricing models: charge by acre/hectare plus terrain or urgency multipliers.

Even if drones are involved, the real differentiator is trust and reporting. Farmers and commercial buyers don’t want stories—they want records.

2) AI for input SMEs: demand forecasting that matches Ghana’s seasons

December is planning season for many agribusinesses—budgets, procurement, and credit arrangements. AI can forecast demand for seeds, fertilizers, and crop protection products using:

• last season’s sales
• rainfall patterns and planting windows
• district-level crop cycles
• price trends

A strong forecast reduces dead stock and keeps cash moving. That’s survival for SMEs.

3) AI for aggregators and processors: traceability that’s not painful

Traceability requirements keep rising, especially for export-linked value chains (cocoa, shea, cashew, horticulture). SMEs often treat this like paperwork. It shouldn’t be.

AI-driven workflows can:

• standardize farmer onboarding
• flag missing data early
• generate batch-level reports automatically
• reduce manual errors in weighbridge and warehouse records

The best systems are boring in a good way: they quietly produce clean data every day.

A Ghana-ready checklist: choosing agtech that won’t trap you

The direct answer: buy systems that keep you flexible—hardware can change, but your data and operations must continue.

Use this checklist before you adopt drones, sensors, or AI farm management software.

Vendor and ecosystem

• Do they have local maintenance partners and spare parts plans?
• Can they train your team in 2 weeks, not 6 months?
• What happens if they stop selling in Ghana—do you still operate?

Data ownership and portability

• Can you export all records (spray logs, maps, farmer lists, invoices)?
• Is there an API or at least structured reports?
• Who owns the data legally—your SME or the vendor?

Connectivity and reliability

• Can it work offline and sync later?
• Is it usable on low-cost Android devices common in Ghana?

Compliance and proof

• Can it produce an audit-ready report for regulators or buyers?
• Does it support standard measurements (hectares, liters, active ingredient)?

Security and ethics

• Is user access controlled (roles for staff, supervisors, auditors)?
• Are there clear policies on data storage and sharing?

If a vendor can’t answer these clearly, don’t sign the deal.

Where Sɛnea AI fits: local control, real SME outcomes

For SMEs in Ghana, the best AI strategy is not to chase every new device. It’s to build a stable digital spine: records, workflows, and analytics that work regardless of which hardware brand is available.

That’s the gap Sɛnea AI is designed to address in this series: AI tools for Ghanaian SMEs that prioritize usability, ethical data handling, and continuity. I’m opinionated about this—if farmers and agribusinesses don’t control their operational data, they’ll always be negotiating from a weak position.

The DJI uncertainty shows what happens when a market grows fast but governance, interoperability, and local alternatives don’t grow with it.

What to do next:

• If you run a spraying service, start by standardizing your application records (even before upgrading equipment).
• If you’re an input SME, set up basic demand forecasting and inventory alerts.
• If you’re a cooperative or aggregator, prioritize farmer data quality and batch traceability.

A final thought for 2026 planning: when Ghana’s agtech adoption accelerates, will your business be tool-dependent or system-driven?