How is tea made using technologies like Vision AI?

For many of us, tea is more than just a fun beverage. It is a daily ritual, a source of comfort, and a quiet companion in our routines. At Ultralytics, we love tea too, especially a perfectly whisked matcha latte. 

But, how often do we think about how tea is made and how it actually makes its way from fields to a cup? Behind every sip is a surprisingly complex process that involves delicate harvesting, careful sorting, and precise packaging. 

Despite tea’s global popularity, the tea industry still relies heavily on manual production processes. From plucking and sorting to grading and packaging, many critical steps are carried out by hand. This results in slower production and sometimes inconsistent quality.

Tea manufacturers are starting to turn to technology to meet growing demand and improve efficiency. With the help of AI and computer vision, technology that enables machines to interpret and analyze visual information, many of the manual steps in tea production can now be automated. 

For instance, computer vision models like Ultralytics YOLO11, which support tasks such as object detection, image classification, and instance segmentation, can be used to identify and sort tea leaves, detect defects, and monitor quality in real-time. These capabilities can step in and streamline operations, reduce human error, and maintain consistent product standards from farm to factory.

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In this article, we’ll take a closer look at how tea is made, why some traditional methods can fall short, and how computer vision is helping bring new speed, precision, and innovation to the way tea is produced. Let’s get started!

A quick look at the history of tea

Before we dive into how tea is made, let's take a quick look at the history of how it became so popular.

Tea has been enjoyed for thousands of years. Its story begins in ancient China, where, according to legend, Emperor Shen Nong accidentally discovered it when tea leaves fell into his boiling water. People quickly realized that the drink was not just refreshing but also offered health benefits. Over time, tea became a central part of Chinese culture and daily life.

From China, tea spread to neighboring countries like Japan and Korea. Each region developed its own unique customs and rituals around tea, turning it into much more than just a beverage. 

In the 1600s, tea made its way to Europe through trade routes and quickly gained popularity, especially in Britain. As drinking tea became a daily habit there, the British established large tea plantations in India and Sri Lanka to meet the growing demand. This helped make tea more affordable and accessible around the world.

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Today, the tea industry continues to thrive, with the global tea market value set to reach approximately $75.5 billion by 2029. Tea is enjoyed by billions of people across the globe and remains deeply rooted in cultural traditions and daily routines for many communities.

The basics: How is tea made? What is tea made of?

Tea is made from the leaves of a plant called Camellia sinensis. Whether you’re drinking black tea, green tea, oolong, or white tea, it all comes from the same plant. The key difference between these varieties is how the leaves are processed after they’re picked. Factors like how long the leaves are exposed to air, how they’re dried, and whether they’re steamed or rolled all affect the flavor and style of the tea.

The tea production process begins with picking fresh, young leaves. After harvesting, the leaves are left to wither. This step reduces moisture and makes them easier to handle. 

The next stage is rolling, which gently twists and breaks the leaves, releasing natural enzymes that break down the leaves. This leads to tea oxidation. When exposed to air, the leaves darken and develop their flavor. 

Black tea is fully oxidized, giving it a rich taste and deep color. Green and white teas undergo minimal or no oxidation, which allows them to remain lighter and more delicate. After tea oxidation, the leaves are dried, sorted, and packed.

Challenges in the manual tea manufacturing process

Even today, tea production relies heavily on manual labor. Tasks like picking, sorting, and packing are still done by hand in many parts of the industry. While these traditional methods have been used for generations, they can slow workflows down and leave room for human error.

Here are some common challenges tea producers face:

• Inconsistent quality: When sorting and grading are done by hand, results can vary depending on who’s doing the work and under what conditions.
  
• Labor-intensive: Processes like plucking and packing take time and require a steady workforce with the right skills.
  
• Slower output: Manual quality inspections and packaging often limit how quickly tea can be produced, making it harder to meet demand.
  
• Missed defects: Damaged leaves, contamination, or faulty packaging can sometimes slip through unnoticed, affecting overall product quality.
  
• Strict standards: In export markets, the smallest mistakes can result in rejected shipments or lower prices.

The need for computer vision in the tea production process

At every stage of the tea manufacturing process, checking the quality of the leaves is essential. These inspections are often done by hand, which can be time-consuming and occasionally inconsistent.

Small differences in the size, shape, or color of tea leaves can affect the flavor and overall grade. When working with large volumes of tea, maintaining consistent quality across every batch becomes a real challenge. This adds complexity to the process and can lead to delays or mistakes.

Computer vision is a reliable solution to these issues. It enables machines to quickly and accurately inspect and analyze tea leaves. 

For example, computer vision models like YOLO11 can be trained to check tea leaves for grading and sorting. This keeps quality steady across different batches. Similarly, machines integrated with YOLO11 can detect and remove harmful leaves, dirt, or other contaminants that could compromise the tea’s quality. 

Applications of computer vision in the tea production process

Now that we have a better understanding of how computer vision is used in tea production, let’s explore some real-world use cases where it’s making a difference in the tea industry.

Robots and drones in Longjing tea harvesting

At Hangzhou, China, where the well-known West Lake Longjing tea is grown, cutting-edge innovation is reinventing traditional tea farming. During the busy harvest season, farmers are using advanced tools like drones, robot dogs, and wearable exoskeletons to make their work more efficient and less physically demanding.

One of the most impactful technologies being used is computer vision. Drones equipped with vision systems fly over the tea fields to monitor plant health and identify which areas are ready for harvest. Instead of walking through the entire plantation, farmers can now get a quick, detailed view of the crop conditions from above, saving time and improving accuracy.

Meanwhile, on the ground, robot dogs with built-in cameras use computer vision to navigate steep, narrow paths while carrying freshly picked tea buds. This helps reduce the physical burden on workers and speeds up delivery to processing stations. Farmers are also wearing robotic exoskeletons that are calibrated to support their legs and movements, making it easier to carry loads like fertilizer or harvested leaves up challenging terrain.

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Similarly, in other tea plantations across China, drones are being used to spray pesticides, a task that was previously done by hand. With the help of computer vision and GPS, these drones can accurately identify target areas, avoid obstacles, and apply pesticides only where needed. This makes the process faster, safer for workers, and more efficient, especially in areas that are difficult to reach or have uneven terrain.

Sorting teas with computer vision systems

In factories where tea is processed, sorting machines are now being integrated with computer vision systems. These machines use high-resolution cameras and image processing techniques to inspect tea leaves as they move along the production line. One key technique used in this process is instance segmentation, which identifies each individual tea leaf in an image and draws a clear outline around it, even when multiple leaves are touching or overlapping.

Based on features like shape, size, color, and texture, the system sorts the leaves into different categories. Good-quality leaves are separated from those that are broken, discolored, or too small. Foreign materials, such as stems or debris, are also identified and removed. This approach brings greater consistency to the sorting process, reduces human error, and supports higher standards in large-scale tea production.

Pros and cons of computer vision in tea production

Here are some benefits of integrating computer vision into the tea production process:

• Lower labor demand: Automating repetitive tasks reduces the need for large teams of manual workers, especially during peak harvest seasons.
• Yield estimation: Vision-powered drones and field cameras can estimate tea leaf growth and predict harvest volume more accurately than manual observation. 
• Packaging quality control: Vision systems can check that tea bags are sealed properly, aligned correctly, and packed in the right order, reducing errors and improving presentation.

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On the other hand, here are some challenges that come with using computer vision in tea production:

• Large training datasets: Accurately sorting tea leaves by grade, such as one-bud-two-leaves, requires hundreds of well-labeled images to train models effectively.
• Differences between tea varieties: Models trained on one type of tea, like Assam, may struggle with others such as Darjeeling or Kenyan tea due to variations in leaf shape, size, and color.
• Factory conditions: Inconsistent lighting, dust on lenses, or cluttered backgrounds can interfere with image quality and reduce detection accuracy during sorting or inspection.

Key takeaways

Computer vision is changing the way tea is produced. It assists with tasks like sorting leaves, checking for foreign objects, and inspecting packaging. These tools improve the speed and accuracy of tea processing while reducing human error.

From the field to the factory, computer vision enables safer spraying, more effective harvest planning, and cleaner final products. As the demand for tea continues to grow, these technologies provide a smarter and more consistent method for producing high-quality tea at scale.

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