DEV Community: Jannatul Nisa Jeem

Agriculture is becoming increasingly connected to software

Jannatul Nisa Jeem — Wed, 19 Aug 2026 16:38:31 +0000

Agriculture is becoming increasingly connected to software, data, and artificial intelligence One interesting application is plant monitoring.

At first glance, plant monitoring seems like something that can be handled entirely through manual observation. But when the number of plants increases, collecting observations consistently and keeping track of changes becomes much harder.

This is where AI and digital monitoring can become useful.

The Problem With Manual Monitoring

Manual inspection remains important, but it has limitations.

A grower may need to repeatedly check plants, record observations, compare previous conditions, and identify changes. Doing this consistently across a large operation can require significant time and effort.

There is also a difference between noticing something once and having a structured history of what happened over time.

Digital monitoring can help address the second problem.

Building a Better Record

One potential advantage of AI-powered plant monitoring is the ability to organize information over time.

Instead of treating every observation as an isolated event, a digital system can help create a more structured record.

That information may make it easier to:

Track changes
Compare observations
Identify unusual patterns
Review historical information
Support agricultural decision-making

The important point is that AI doesn't have to make every decision itself.

It can instead help transform raw or scattered information into something more useful for people.

Why This Matters at Scale

Consider the difference between monitoring ten plants and monitoring thousands.

The basic process may be similar, but the amount of information becomes dramatically different.

As agricultural operations scale, maintaining consistent monitoring becomes more challenging. Automation and AI can potentially help reduce some of the burden involved in organizing and interpreting plant-related information.

This is one reason AI in agriculture is interesting beyond the hype surrounding the technology itself.

AI Should Support, Not Replace, Expertise

Agriculture isn't a simple data problem.

Weather, soil conditions, plant varieties, environmental factors, and farming practices can all affect plant health.

Because of this, AI-generated information should be treated as a tool that supports human expertise rather than an unquestionable answer.

A useful agricultural technology should help people understand their plants better and make more informed decisions.

Exploring the Idea

PlantLogAI is an example of a platform focused on applying AI to plant monitoring. You can explore the concept here:

https://plantlogai.com/

The broader lesson is worth considering for developers working on AgTech: useful agricultural software doesn't necessarily need to replace existing workflows. Sometimes its greatest value can come from making information easier to collect, organize, understand, and use.

As AI continues moving into agriculture, plant monitoring is an interesting example of how software can connect data with real-world decision-making.

From Connected Devices to Useful Intelligence: The Real Challenge of Industrial IoT

Jannatul Nisa Jeem — Wed, 19 Aug 2026 15:13:04 +0000

From Connected Devices to Useful Intelligence: The Real Challenge of Industrial IoT

Those technologies are important, but there is another question that matters just as much:

What happens after the data is collected?

Connecting physical assets to a digital system is only the beginning. The real value comes from turning that information into something people can actually use to understand and improve industrial operations.

IoT Creates the Data Layer

Industrial environments can contain equipment, vehicles, inventory, tools, facilities, and other physical assets that generate useful information.

IoT technologies can help capture information about these assets and their operating environments.

For example, connected systems can provide information about:

Asset location
Equipment activity
Inventory movement
Environmental conditions
Operational events
Equipment status

This creates a connection between the physical environment and digital systems.

But collecting information alone doesn't automatically solve an operational problem.

The Data Problem

Imagine an organization successfully connects thousands of assets.

It now has thousands—or potentially millions—of data points.

That sounds valuable, but the organization may still have difficulty answering basic questions:

Which information matters?
Is the data reliable?
What patterns should the team pay attention to?
Which events require action?
How does this information connect with existing workflows?

This is where the difference between data collection and operational intelligence becomes important.

Where AI Can Help

AI can provide another layer on top of connected systems.

Instead of relying entirely on people to manually review large amounts of operational information, AI can help analyze data and identify patterns, anomalies, or relationships that may deserve attention.

The goal isn't necessarily to replace human decision-making.

In many industrial applications, the more practical objective is to give people better information so they can make decisions with greater awareness of what is happening in the physical environment.

That makes the combination of AI and IoT particularly interesting.

IoT helps answer:

What is happening in the physical environment?

AI can help answer:

What might this information mean?

Start With the Problem, Not the Technology

One mistake organizations can make is starting an IoT project simply because connected technology is available.

A better approach is to begin with a specific operational challenge.

For example, an organization might want to improve:

Asset visibility
Inventory management
Equipment monitoring
Operational awareness
Workforce monitoring
Supply-chain visibility

Once the problem is understood, teams can determine what information is required, how that information should be collected, and whether AI can help analyze it.

This can also prevent organizations from collecting large quantities of information that ultimately has little practical value.

Building Systems Around Real Operations

Industrial technology also has a different requirement from many purely digital applications: it has to work in the physical world.

Industrial environments can be complex. Equipment, people, facilities, connectivity, existing software, and operational processes all interact.

That means an effective AIoT system needs to consider more than just the software layer.

The sensors and connected devices have to produce useful information. Data needs to move through appropriate systems. AI models need relevant information to work with. And the resulting insights need to fit into real operational workflows.

This is why a system-first approach can be valuable when developing industrial AIoT solutions.

Aperture Venture Studio describes this approach in the context of building AIoT systems for real industrial applications. Their overview can be found here.

The Future Is More Than Connectivity

Industrial IoT has evolved beyond the simple idea of connecting machines to the internet.

The more interesting opportunity is creating systems where connected physical environments continuously produce information that can support better decisions.

That requires a combination of:

Physical systems → Connectivity → Data → Intelligence → Action

If one of those layers is missing, the overall system may not deliver its intended value.

The challenge for industrial organizations isn't simply to collect more data.

It's to determine which data matters, how it should be interpreted, and how it can improve real-world operations.

That is where the combination of IoT and AI becomes particularly compelling.

Using AI to Understand Manufacturing Logistics

Jannatul Nisa Jeem — Tue, 18 Aug 2026 15:54:26 +0000

A manufacturing plant can have plenty of inventory and still experience material-related delays.

That sounds strange at first, but it's a common operational challenge: the material exists, but the team doesn't know exactly where it is, whether it's being transported, or when it will reach the production line.

This is where real-time visibility can make a difference.

It's more than tracking

Technologies such as RFID, BLE, UWB, and RTLS can help manufacturers understand where materials, containers, WIP, forklifts, and other assets are located.

But simply putting a tracker on an asset isn't the end goal.

The more useful question is:

What can we learn from the movement data?

For example, if a particular material repeatedly spends a long time in a staging area, that could point to a transportation or replenishment issue.

If forklifts repeatedly take inefficient routes, there may be an opportunity to improve internal material flow.

If WIP consistently waits too long between production stages, the data may reveal a bottleneck that isn't obvious from production reports alone.

Where AI fits in

AI can help analyze these patterns across large amounts of operational data.

Instead of looking at inventory, production, and location information separately, manufacturers can connect these sources and look for relationships.

This can support use cases such as:

Predicting potential material shortages
Improving replenishment planning
Understanding WIP movement
Identifying logistics bottlenecks
Improving asset utilization
Finding recurring transportation inefficiencies

The important part is that AI should be tied to a real operational problem.

Adding AI to a process simply because it is a popular technology doesn't guarantee a useful result.

Connecting different systems

Manufacturing environments often have several systems working at the same time.

ERP may contain inventory and business information.

MES may contain production information.

WMS may manage warehouse operations.

IoT and location systems may provide information from the physical environment.

When these systems remain isolated, it can be difficult to understand the complete flow of materials.

Connecting the information can provide a much clearer picture of what is happening between the planned process and the actual process on the factory floor.

PlantLog AI is one example of an approach that combines AI, industrial IoT, and location intelligence for in-plant logistics.

Start with the problem

Before choosing a tracking technology or AI solution, it makes sense to identify the problem first.

Ask:

Where are production delays coming from?
How often do workers search for materials?
Which assets are difficult to locate?
Where does WIP tend to accumulate?
How predictable is replenishment?
Which logistics activities consume the most time?

Once those questions are clear, technology becomes much easier to evaluate.

The goal isn't to create a factory that collects endless amounts of data.

The goal is to create a factory where the available data helps people understand what is happening and make better decisions.

That's where real-time visibility, industrial IoT, and AI can become genuinely useful.

Beyond Tracking: Using AI to Understand Manufacturing Logistics

Jannatul Nisa Jeem — Tue, 18 Aug 2026 15:53:49 +0000

A manufacturing plant can have plenty of inventory and still experience material-related delays.

This is where real-time visibility can make a difference.

It's more than tracking

Technologies such as RFID, BLE, UWB, and RTLS can help manufacturers understand where materials, containers, WIP, forklifts, and other assets are located.

But simply putting a tracker on an asset isn't the end goal.

The more useful question is:

What can we learn from the movement data?

For example, if a particular material repeatedly spends a long time in a staging area, that could point to a transportation or replenishment issue.

If forklifts repeatedly take inefficient routes, there may be an opportunity to improve internal material flow.

If WIP consistently waits too long between production stages, the data may reveal a bottleneck that isn't obvious from production reports alone.

Where AI fits in

AI can help analyze these patterns across large amounts of operational data.

Instead of looking at inventory, production, and location information separately, manufacturers can connect these sources and look for relationships.

This can support use cases such as:

Predicting potential material shortages
Improving replenishment planning
Understanding WIP movement
Identifying logistics bottlenecks
Improving asset utilization
Finding recurring transportation inefficiencies

The important part is that AI should be tied to a real operational problem.

Adding AI to a process simply because it is a popular technology doesn't guarantee a useful result.

Connecting different systems

Manufacturing environments often have several systems working at the same time.

ERP may contain inventory and business information.

MES may contain production information.

WMS may manage warehouse operations.

IoT and location systems may provide information from the physical environment.

When these systems remain isolated, it can be difficult to understand the complete flow of materials.

Connecting the information can provide a much clearer picture of what is happening between the planned process and the actual process on the factory floor.

PlantLog AI is one example of an approach that combines AI, industrial IoT, and location intelligence for in-plant logistics.

Start with the problem

Before choosing a tracking technology or AI solution, it makes sense to identify the problem first.

Ask:

Where are production delays coming from?
How often do workers search for materials?
Which assets are difficult to locate?
Where does WIP tend to accumulate?
How predictable is replenishment?
Which logistics activities consume the most time?

Once those questions are clear, technology becomes much easier to evaluate.

The goal isn't to create a factory that collects endless amounts of data.

The goal is to create a factory where the available data helps people understand what is happening and make better decisions.

That's where real-time visibility, industrial IoT, and AI can become genuinely useful.

How AI Is Making Manufacturing Logistics Smarter

Jannatul Nisa Jeem — Tue, 18 Aug 2026 15:16:44 +0000

Manufacturing plants generate an enormous amount of operational data every day.

Machines produce data. ERP and MES systems record production information. Warehouses track inventory. RFID tags and location systems can track materials and assets.

Yet many plants still struggle with a surprisingly simple question:

"Where is everything, and what is going to happen next?"

That's where AI-powered in-plant logistics is becoming interesting.

The problem with traditional tracking

Basic tracking systems are useful. If you need to find a pallet, container, or tagged asset, knowing its last recorded location is already valuable.

But manufacturing logistics is more complicated than simply finding things.

A material might be available in inventory but sitting in the wrong area. A production line might be waiting for a component that technically exists somewhere inside the facility. A forklift might be spending too much time traveling between the same locations.

These aren't necessarily inventory problems. They're visibility and material-flow problems.

Where AI can help

AI can analyze information from different sources and identify patterns that are difficult to see manually.

For example, a manufacturing operation could combine:

Inventory data
Production schedules
RFID data
RTLS/location data
Forklift movement
WIP information
Replenishment activity
ERP/MES/WMS data

The goal isn't to collect data just because it is available.

The goal is to turn that data into useful operational information.

1. Predicting material shortages

Instead of waiting for a production worker to report that a component is missing, AI can analyze consumption patterns, current inventory, production requirements, and movement data to identify potential shortages earlier.

This can make replenishment more proactive.

2. Understanding WIP

Work-in-process can be difficult to track because it constantly moves through different production stages.

Location data can show where WIP is currently located, while historical information can help reveal how long it typically stays in certain areas.

That can make recurring bottlenecks easier to investigate.

3. Improving asset utilization

Forklifts, carts, containers, AGVs, and other mobile assets are part of the logistics process.

Tracking their movement can reveal:

Excessive travel
Long idle periods
Repeated routes
Congested areas
Uneven utilization

AI can help identify patterns and provide information that operations teams can use when improving the process.

RFID, BLE, UWB, or RTLS?

There isn't one technology that is automatically right for every factory.

RFID can be useful for identifying and tracking tagged items.

BLE can support location and proximity-based applications.

UWB can provide highly accurate positioning in suitable environments.

RTLS is a broader approach to real-time location tracking that can use different technologies depending on the implementation.

The important thing is to start with the problem rather than the technology.

Ask:

What needs to be tracked?

How accurate does the location need to be?

How often does the item move?

What decision will the data help us make?

Those questions are often more important than choosing a technology based purely on its features.

Connecting the plant floor

Another major challenge is that manufacturing data is often fragmented.

ERP, MES, WMS, IoT platforms, tracking systems, and production equipment may all contain useful information, but they don't necessarily provide one unified picture.

Connecting these sources can help bridge the gap between what the system says should be happening and what is actually happening on the plant floor.

That's the direction platforms such as PlantLog AI are taking by combining AI, industrial IoT, location intelligence, and in-plant logistics.

AI isn't the point—the outcome is

It's easy to get caught up in the technology.

AI, IoT, RTLS, UWB, edge computing and other technologies are interesting, but they aren't the actual goal.

The goal is solving operational problems.

Maybe employees are spending too much time searching for materials.

Maybe replenishment is too reactive.

Maybe WIP keeps getting delayed in the same area.

Maybe forklifts are not being used efficiently.

Maybe different systems don't provide a consistent view of what's happening.

Those are the problems worth solving.

The best manufacturing AI implementations aren't necessarily the ones using the most complicated technology. They're the ones that turn real operational data into decisions that improve the way the plant runs.

Better tracking is useful. Better understanding is even more valuable.

Manufacturing plants generate an enormous amount of operational data every day. Machines produce data. ERP and MES systems record production information. Warehouses track inventory. RFID tags and location systems can track materials and assets. Yet many

Jannatul Nisa Jeem — Tue, 18 Aug 2026 15:15:55 +0000

Before Building an AI System, Ask These Questions

Jannatul Nisa Jeem — Mon, 17 Aug 2026 20:16:04 +0000

AI is everywhere right now.

Businesses are experimenting with AI for analytics, automation, customer support, forecasting, and many other applications.

But before building or adopting an AI system, there are some basic questions worth asking.

The first one is surprisingly simple:

What problem are we trying to solve?

Is There Actually a Problem?

This sounds obvious, but it's easy to overlook.

If a company can't clearly explain what it wants to improve, adding AI may not solve anything.

A useful project should have a specific objective.

Maybe the company wants to identify unusual equipment behavior.

Maybe it wants to reduce repetitive manual work.

Maybe it wants to understand large amounts of operational information.

A clear problem makes it much easier to determine whether AI is appropriate.

Do We Have Useful Data?

AI depends heavily on information.

If the data is incomplete, inconsistent, outdated, or inaccurate, the results may not be reliable.

This is particularly important for businesses working with IoT and connected devices.

Sensors may generate huge amounts of information, but quantity isn't the same as quality.

Before thinking about an advanced model, it's worth understanding where the data comes from and whether it represents the real-world situation accurately.

Would a Simpler Solution Work?

Not every problem requires AI.

Sometimes a straightforward rule or monitoring system can solve an issue perfectly well.

For example, if a business simply needs to know when a measurement crosses a clearly defined limit, a basic alert may be enough.

Using AI for a problem that doesn't require it can add unnecessary complexity.

The goal should be the simplest solution that reliably solves the problem.

Who Will Use the Result?

This question is often forgotten.

Suppose an AI system identifies an unusual pattern.

What happens next?

Does an engineer receive the information?

Does a manager review it?

Does another system automatically respond?

If nobody knows what to do with the result, the prediction has limited practical value.

AI should fit into an existing workflow or help create a better one.

Can the System Grow?

A successful prototype might work with a small amount of data and a few users.

But what happens when the organization grows?

More devices may be connected.

More information may be generated.

More employees may need access.

The system may need to integrate with other platforms.

Thinking about scalability early can prevent difficult redesigns later.

Technology Should Support People

It's easy to focus on models, platforms, and technical capabilities.

But technology ultimately exists to help people accomplish something.

A good AI system should make information easier to understand, reduce unnecessary work, identify useful patterns, or support better decisions.

The most impressive technical solution isn't always the most valuable one.

Sometimes the best solution is the one that quietly solves a problem without making everything else more complicated.

That's a useful principle not only for AI, but for technology projects in general.

For more information about technology, innovation, and venture building, explore Aperture Venture Studio.

IoT Isn't About Connecting Everything ,It's About Connecting What Matters

Jannatul Nisa Jeem — Mon, 17 Aug 2026 20:14:08 +0000

When people hear "IoT," they often imagine thousands of sensors, smart machines, real-time dashboards, and massive amounts of data.

That picture isn't completely wrong.

But I think it misses the most important part.

The purpose of IoT isn't to connect everything. It's to make useful information available at the right time.

Start With a Problem

A common mistake in technology projects is starting with the technology.

"We should use IoT."

"We should add AI."

"We need a smart system."

Those statements don't explain what the business actually needs.

A better starting point is a problem.

Maybe a company is struggling with unexpected equipment downtime.

Maybe employees are spending too much time manually checking machines.

Maybe a business doesn't know how its assets are being used.

Maybe important operational information is arriving too late.

Once the problem is clear, connected technology becomes easier to evaluate.

What IoT Can Actually Provide

Connected devices can provide visibility into physical environments.

Depending on the use case, businesses can monitor equipment conditions, environmental changes, asset activity, energy usage, and other operational information.

That can be valuable because physical processes aren't always easy to observe continuously.

A person might inspect a machine once every few hours.

A connected system can potentially provide information much more frequently.

The benefit isn't necessarily that the system collects more data.

The benefit is that people can have a better understanding of what's happening.

Data Quality Still Matters

There's an important catch.

If the information being collected isn't reliable, the conclusions based on it may not be reliable either.

Sensors can produce incorrect readings.

Devices can disconnect.

Data can arrive late.

Information can sometimes be duplicated.

That's why building a connected system isn't just about installing devices.

You also need to think about how the information is collected, validated, stored, and interpreted.

Where AI Fits

AI and analytics can add another layer of value.

Imagine having years of information about equipment behavior.

There may be patterns that aren't obvious when looking at individual readings.

Analytics can help identify unusual behavior, while AI can potentially help recognize more complex patterns.

But AI shouldn't be treated as the entire solution.

A prediction is only useful if someone understands it and can act on it.

The complete system matters more than any individual technology.

Don't Forget the People

One of the most interesting parts of IoT is that the final decision often still involves a person.

A system might indicate that something unusual is happening.

An engineer may need to investigate.

A manager may need to decide whether maintenance should happen immediately.

The technology provides information.

Human experience provides context.

That's why good IoT systems should be designed around the people who will actually use the information.

Keep It Practical

You don't need thousands of devices to prove that an idea works.

A small pilot can help answer important questions:

Does the information actually help?

Are the readings reliable?

Will employees use the system?

Does it solve the original problem?

What happens when the network goes down?

Answering these questions early can prevent organizations from spending heavily on a solution that doesn't provide enough value.

IoT is exciting, but the most successful projects aren't necessarily the most complicated ones.

They're the ones where the technology solves a real problem.

For more perspectives on technology, innovation, and venture building, visit Aperture Venture Studio.

What I Like About Working With Physical Data

Jannatul Nisa Jeem — Sun, 16 Aug 2026 18:10:40 +0000

There's something satisfying about software that reacts to something happening outside the computer.

With a normal application, I might click a button and see something change on a screen.

With an IoT system, the chain can be:

Something changes physically
↓
Sensor notices it
↓
Device sends data
↓
Software processes it
↓
System responds

For example, a temperature changes.

A sensor notices.

The backend receives it.

An alert gets triggered.

Someone gets notified.

That's a completely different feeling from building another CRUD application.

You're writing software, but the software is interacting with the physical world.

That's probably one of the biggest reasons I find IoT interesting.

Related resource: Aperture Venture Studio

A Device's "Last Seen" Timestamp Is Surprisingly Useful

Jannatul Nisa Jeem — Sun, 16 Aug 2026 18:09:01 +0000

Here's a very simple piece of data:

last_seen

That's it.

But it can tell you a lot.

If a device hasn't communicated for 30 seconds, maybe that's normal.

If it hasn't communicated for six hours, something might be wrong.

You can use it for basic device health monitoring:

if now - last_seen > timeout:
mark_device_offline()

Obviously, the timeout depends on the application.

A device that reports once per day shouldn't be considered broken after five minutes.

The nice thing about simple signals like this is that they don't require AI.

Sometimes a timestamp and a sensible rule are enough to solve a real problem.

Related resource: Aperture Venture Studio

Device Firmware Updates Are Basically Software Deployment for Tiny Computers

Jannatul Nisa Jeem — Sun, 16 Aug 2026 18:07:23 +0000

It's easy to think of IoT devices as sensors.

But many modern devices are basically tiny computers.

And just like your laptop or server, their software needs updates.

Maybe there's a security vulnerability.

Maybe there's a bug.

Maybe you added a new feature.

Now imagine you have 5,000 devices installed in different locations.

You're obviously not going to visit every device with a USB cable.

You need some kind of remote update system.

And then more questions appear.

What if the update fails?

What if the device loses power halfway through?

What if the new version has a serious bug?

Can you roll back?

Can you update only 1% of devices first?

Deploying software to physical devices comes with a few extra complications.

Related resource: Aperture Venture Studio

What Happens When a Device Suddenly Sends 10,000 Messages?

Jannatul Nisa Jeem — Sun, 16 Aug 2026 18:06:36 +0000

Let's say a device normally sends one message every minute.

Then something goes wrong.

It reconnects after being offline and suddenly sends thousands of stored readings.

Your backend was designed for normal traffic.

Now you have a spike.

This is why IoT systems need to think about bursts, not just average traffic.

You might need:

Message queues
Rate limiting
Backpressure
Batch processing
Autoscaling

A message broker can help separate the device from the services processing the data.

Instead of:

Device → Processing Service

you can have:

Device
↓
Message Broker
↓
Processing Workers

The broader lesson isn't just about IoT.

It's a distributed-systems lesson:

Design for what happens when everything happens at once.

Related resource: Aperture Venture Studio