Coffee industry’s top tech stories in 2024 Part 2: Artificial intelligence

Perry Luckett CoffeeMan1

In our last coffee blog post, I covered how automated coffee harvesting technology will continue to improve production in 2024 and beyond. Today’s topic is another major enhancement for coffee production: artificial intelligence (AI)-driven quality control. Later, I’ll bring you up to date on Internet of Things (IoT) and blockchain technologies, which can help make coffee supply chains efficient, transparent, and traceable.

Artificial intelligence could pose problems for small-farm producers

Although most of this review considers how AI can greatly benefit coffee producers, it also puts a lot of pressure on indigenous and smallholder coffee farmers. As with many innovative technologies, AI may enhance efficiencies for “corporate” operations while leaving small farms behind. Sarah Charles thoughtfully discusses some of these difficulties in her online article for the Coffee Intelligence Newsletter. [SC]

One example is the rise of “smart agriculture,” which creates more uniform, carefully controlled crops that depend on consolidation of farming, technology, and big data industries. These techniques feed a growing global market that demands expensive use of fertilizers, chemicals, and corporate seeds. Smallholder farmers unable to access or sustain the technological shift are likely to struggle and lose business. At the same time, technology will slowly replace or at least decrease the value of manual laborers, possibly causing further inequality and potential hunger for the working class.

Local farmer knowledge—which typically accounts for history, climate, culture and politics—also could be under threat from AI. A global AI system will likely draw from artificial intelligence centered in the Global North, which could force “cookie cutter” solutions onto local coffee production and drain the power of indigenous farmers. AI-powered algorithms could also change the pricing and distribution of coffee by predicting coffee prices and eliminating inefficient suppliers. That could harm the income and livelihoods of coffee farmers.

But AI-driven quality control will produce better coffee for large farms and consumers

Using robotics and artificial intelligence (AI) in production processes is revolutionizing the way coffee is produced. By automating many of the labor-intensive tasks associated with its production, AI technology has enabled more efficient processes and higher levels of quality control.

Robotics can help automate repetitive tasks, such as picking and sorting, while AI technology monitors and analyzes each production stage. This use of technology helps reduce waste, increase yields, and improve product consistency. Machine-learning algorithms also can detect anomalies and make predictions that allow faster corrective action.

Of course, some challenges remain. The technologies are very expensive for the coffee industry to adapt and apply. Programming robots also can be complex, which means coffee companies must either employ highly skilled programmers directly or contract out these tasks. Robots and artificial intelligence systems that operate independently can create safety concerns for people working near them. Finally, over-automation or redundancy can occur in certain areas, potentially trying to automate tasks that people can manage better.

Robotics and AI can improve coffee-production processes

The coffee industry is increasingly integrating robots and advanced technology to enhance some stages of coffee production, from farming to brewing. Here’s how:

Farming and harvesting

AI and machine learning algorithms can detect early signs of pest infestations and diseases, allowing for timely interventions that minimize damage and reduce the need for chemical treatments.

Improving coffee production and quality control

Let’s dive into the world of data-driven coffee production and quality control. Achieving efficiency and quality assurance are key goals in this domain. By leveraging data analytics, machine learning, and predictive analytics, we can optimize our production processes to make sure that we’re producing the highest quality coffee at the lowest cost.

Data collected from production machines allows us to identify bottlenecks and potential issues before they become costly errors. We can use this data to adjust our production parameters to ensure that every cup of coffee is brewed just right. Additionally, by tracking customer feedback over time, we can identify trends in tastes and preferences so that our products are always tailored to meet the needs of our customers.

Quality assurance is also enhanced through automated systems. Machine learning algorithms can be used to detect defects or inconsistencies with product specifications quickly and accurately so that corrective action can be taken immediately. This ensures that all products meet customer expectations while minimizing waste due to incorrect brewing or inaccurate packaging labels.

Utilizing these advanced technologies can streamline our operations for greater efficiency while ensuring customer satisfaction by delivering consistently high-quality coffee products.

Brewing and serving

Robotic Baristas: In coffee shops, robotic baristas can prepare a wide range of beverages with high precision and consistency. These robots can handle complex tasks such as grinding beans, brewing coffee, and frothing milk, providing a quick and reliable service. Companies like Rozum Café have developed robotic systems capable of making up to 30 beverages per hour, which can greatly reduce labor costs and improve service speed. [WCP]

https://www.reuters.com/

Super-Automatic Machines: These machines are increasingly common in large coffee chains and restaurants, offering high-quality coffee with minimal human intervention. They can replicate the skills of a trained barista, ensuring that every cup meets the same standards without the need for extensive staff training. [WCP]

AI can strengthen quality-control processes

Artificial intelligence is useful for quality control because it can help producers be more effective in every part of their operations. AI-driven quality control for coffee production can apply advanced technologies to ensure consistency, adherence to flavor profiles, and overall product quality.

Here's an overview of how such a system might work:

Optimizing resources for coffee growing

Technologies like AI (plus sensors in the field) monitor and best use resources such as water and energy, leading to more sustainable farming practices. This not only lowers production costs but also minimizes the environmental footprint of coffee production. [SC]

Sourcing raw materials to support production

AI algorithms analyze market trends, weather patterns, and historical data to forecast demand for raw materials. Predictive analytics help to establish inventory levels and procurement strategies, ensuring just-in-time delivery. Data from sources throughout the coffee-production chain includes growing conditions, harvesting techniques, and processing methods. AI can integrate these elements into the system data from previous batches, including evaluations of information from sensors and quality assessments.

Sorting coffee beans automatically for incoming inspections

Coffee beans are traditionally graded by size using sieves and by density using water baths or air flotation systems. AI-based systems can automate this process using sensors to measure bean size and weight in real time. Machine learning models can then classify beans according to their grade based on these attributes. AI also can classify beans into more precise size categories, improving uniformity in roasting and brewing, which is critical for specialty coffee. Thus, automated systems handle large volumes of beans with precision, reducing the reliance on manual labor and the potential for human error. [EO]

When a batch of raw beans arrives at the factory, sensors inspect a sample for quality parameters such as moisture content, bean size, and visual defects. AI-powered sorting machines then analyze images of coffee beans based on parameters such as size, color, and density. They can detect such imperfections as mold, insect damage, or physical defects. Beans that don't meet the quality standards are rejected or sorted for further processing.

Improving coffee roasting profiles

Genio 6 roaster enabled with AI to produce more precise roasting profiles based on linked feedback from coffee tasters.

Advanced machinery, including AI-driven roasting systems, ensures beans roast to perfection based on profiles that bring out desired flavors and aromas. In turn, AI algorithms can analyze data from previous roasts along with real-time sensory feedback to produce the best roasting profiles. By closely monitoring temperature and time, these algorithms can ensure consistency and quality. An example is the Genio 6 Evolution series, a new coffee roasting system that links cupping scores by expert baristas with roast profiles, which are like a recipe that determines the beans’ flavor. This linking enables the system to learn from the feedback of coffee tasters and create a roast profile that perfectly matches a desired taste. [GRS]

Assessing coffee-production quality with computer vision

"Computer vision" in coffee operations uses advanced image-processing and machine-learning techniques to automate and enhance various stages of coffee production, from farming to quality control. The coffee industry is applying this technology in several areas to achieve consistent quality across the supply chain.

Computer vision can inspect and grade coffee beans by analyzing their physical attributes. Systems like those from Agrivero use high-resolution cameras combined with AI to scan and evaluate green coffee beans, detecting defects such as discoloration, mold, cracks, and inconsistencies in color. AI algorithms can identify and classify these defects accurately, enabling quick removal of subpar beans from the production line. As an example, Agrivero delivers 100% accurate results within four minutes per sample.

As Agrivero says on their website, this technology produces benefits for coffee producers; for exporters, importers, and traders; and for coffee roasters: [AS]

Producers can use AI to track changes in batch processing, compare quality reports over time, and share results with traders and roasters. They also can review data to benchmark their results against other producers and to develop and view price estimates for their products.

Exporters, importers, and traders can do fast quality checks on samples at the point of purchase and offer incentives to producers for better quality. They also can organize logistics according to coffee quality and changing weather conditions, which makes them more efficient and productive. Organizing logistics around quality ensures beans of similar quality are processed, stored, and transported together.

Accessing quality and traceability data at every step of the supply chain greatly reduces the subjectivity and labor involved in traditional grading methods. This logistical organization is important because quality levels may require different handling, storage conditions, and roasting processes. Producers can mark high-quality beans for faster shipping to premium markets and may direct lower-quality beans to markets with different standards or use them for different product lines. [EC]

Weather-Responsive Planning: Weather conditions significantly affect coffee production, influencing factors like harvest timing, bean drying processes, and transportation. By integrating weather forecasts and real-time weather data with logistics planning, coffee producers and suppliers can adjust their operations to lower risks. For example, in regions expecting heavy rain, they may need to accelerate drying or move beans to dry storage more quickly. Similarly, if a region is facing a drought, they may adjust logistics to bring water to stressed plants or harvest earlier than usual to prevent crop loss.

Adaptive Supply Chain Management: This approach involves using real-time data and predictive analytics to adjust the supply chain based on current and forecasted conditions. For instance, if weather conditions predict a lower yield in a particular region, logistics might be reconfigured to source coffee from other regions to meet demand. Or, if a bumper crop is expected because of favorable weather, logistics might be scaled up to handle the increased volume efficiently.

Defect Detection: Another application is in detecting defects while processing coffee beans. Systems can be trained to identify and remove foreign objects like stones or poor-quality beans, such as "quakers" (underdeveloped beans), which might otherwise go unnoticed. Programmers use models that are trained on large datasets of coffee bean images, enabling them to differentiate between good and defective beans with high accuracy. [AV]

Precision Agriculture: On the farming side, computer vision can help producers monitor coffee plants, estimate yields, and even detect diseases. By analyzing images of coffee plants, these systems can provide farmers with actionable data, helping them use best cultivation practices, predict harvest times, and ultimately increase productivity. [AV, EC]

Overall, computer vision is playing a transformative role in modernizing coffee operations, making processes more efficient and scalable, while also ensuring higher standards of quality in the final product.

Flavor profiling and blending through sensory analysis

AI can analyze the chemical composition of different coffee beans to predict flavor profiles. It also can analyze sensory data such as aroma, taste, and texture. Sensors or electronic noses then capture aroma profiles, and taste analyzers simulate human taste buds. This data feeds into the AI system to establish benchmarks for quality based on customer favorites and market trends. AI algorithms recommend best blends and proportions that match these preferences.

Real-time Monitoring and Adjustment

Throughout the coffee-production process, AI systems use sensors installed on the production line to continuously monitor parameters such as temperature, humidity, moisture content, and processing time. Any deviations from the most favorable conditions trigger alerts for corrective action. The systems adjust elements immediately to maintain best conditions and quality.

Predicting maintenance needs

Another important function for AI is to predict equipment failures based on data analytics and sensor readings, allowing for advance maintenance. By decreasing downtime and preventing unexpected breakdowns, predictive maintenance ensures consistent production and quality.

Analyzing consumer feedback

AI-powered chatbots offer customers support around the clock by instantly handling inquiries, tracking orders, and resolving issues. Meanwhile, natural language processing (NLP) algorithms can analyze customer feedback to discover areas for improvement in the system’s responses. Beyond chatbot operations, AI-powered sentiment-analysis tools can evaluate customer feedback from other sources, such as social media and online reviews. By identifying patterns and trends in customer feedback, producers learn about consumer preferences and improve product quality.

Enhancing supply chains

AI algorithms can improve supply-chain logistics by predicting demand, adjusting inventory levels, and identifying the most efficient shipping routes. These algorithms review historical sales data, market trends, and external factors to generate accurate demand forecasts. Then, machine-learning models dynamically alter forecasts based on changing market conditions, increasing accuracy over time. AI-powered algorithms plan the most efficient delivery routes, considering things like traffic patterns, weather conditions, and delivery windows. They adjust transportation schedules and manage fleets for lowest cost and fastest delivery speed. This intelligent processing sources raw materials efficiently, deliver products promptly to consumers, and help maintain quality standards.

Managing regulatory compliance and certification

AI-driven quality control systems can track the journey of coffee beans from farm to cup. They also can automate compliance checks so producers meet industry standards and regulations, such as Fair Trade or Organic certifications. Automated checks reduce administrative burdens and cut the risk of human error.

Applying continuous learning and improvement

Coffee producers must learn from historical data to improve processes, meet quality standards, and operate effectively in an increasingly competitive world marketplace. An AI-driven system can use feedback from quality assessments, customer feedback, and market trends to continually refine its models and algorithms. By leveraging machine-learning algorithms, producers can adapt quickly to changing market conditions and consumer preferences.

Futuristic illustration of the coffee supply chain, using AI technology at each major stage. From AI-enhanced farming practices to automated roasting and AI-powered retail experiences, this image captures a high-tech vision of coffee production and distribution. [ChatGPT4.0 and DALL-E]

Overall, AI-driven processes developed for coffee production combine data analytics, machine learning, and sensory analysis to ensure consistent quality, make production most efficient, and meet expectations of consumers like us. Applying robotics and advanced technologies in the coffee industry aims to improve efficiency, consistency, and sustainability—all of which leads to that fine product in our cups.

Resources

Agrivero Staff, “Agrivero.ai-AI-enabled solution for green coffee grading & actual traceability,” https://agrivero.ai/, 2024. [AS]

Elisa Criscione, “Why data quality matters in coffee digitalization: five tools that check for inconsistencies,” https://bit.ly/49wok59, DigitalCoffeeFuture.com, 2024.  [EC]

Sarah Charles, “AI will shape the future of coffee production – but who stands to benefit?” Coffee Intelligence Newsletter, https://bit.ly/3D4kzYv, May 11, 2023.  [SC]

Genio Roasters Staff, “Genio 6 Evolution AI-enabled Smart Coffee Roaster,” https://bit.ly/3D8SiQm, 2024. [GRS]

Unknown, “Automation and Robotics in Coffee Sorting and Grading: Assessing the Potential of Machine Learning and AI Technologies,” https://bit.ly/4giFOUG, Expressooutlet.com, August 27, 2024. [EO]

Unknown, “The new generation of automated coffee concepts – Pt. 1,” https://bit.ly/4g4QfeB, Allegra World Coffee Portal, September 30, 2021.  [WCP]

Abirami Vina, “Computer Vision Use Cases in Robotics,” https://bit.ly/3Bm0fRG, Roboflow.com, Oct 31, 2024. [AV]