Assessing quality in the coffee industry has been a challenging task, and setting standards throughout the supply chain has proven to be a complex task. International trade standards require a meticulous screening process to identify problems such as discoloration, mold growth, insect damage and skin defects before roasting.
Traditionally, green coffee buyers requested samples to assess the quality of the product. The green coffee bean sampling process is structured into several stages of the sales cycle, progressing from type samples to stock samples, followed by offer samples, and finally concluding with pre-shipment samples.
Certified cupping experts, known as Q Graders, are responsible for evaluating coffee quality through physical and sensory assessments, including color, aroma and flavor examinations. Despite its advanced training and reliance on the industry standard coffee cupping wheel, this method presents inherent subjectivity, posing challenges to consistency and objectivity in quality assessment. The classification process involves roasting, grinding and fermentation, adding complexity to an already complex evaluation.
At the farm level, a palpable lack of access to essential knowledge and tools leaves coffee producers with a limited understanding of how they can improve the quality of their crops. This information deficit perpetuates a cycle of uncertainty and missed opportunities. It harms coffee producers' ability to understand the true value of their crop and obstructs their ability to implement improvements. These issues create a significant gap in the potential for quality improvement across the coffee value chain.
The role of AI in accurately controlling coffee quality
Artificial Intelligence (AI) represents a transformative force with global implications in several sectors, offering solutions that revolutionize processes and increase efficiency. In the coffee industry, AI emerges as a game changer, promising to improve not only the efficiency of classification processes, but also the overall quality of the final product.
The complex and subjective nature of traditional methods highlights the need for a more precise and simplified approach to controlling coffee quality. With its machine learning algorithms and computer vision technologies, AI introduces a level of accuracy that surpasses what conventional methods can achieve. Emerging AI-based solutions enable rapid and accurate assessment of green coffee samples, alleviating some of the inefficiencies of traditional quality assessment processes.
One of the general objectives of these tools is to collect and analyze data on a large scale. This data-driven approach facilitates informed decision-making across the entire coffee value chain. Producers and cooperatives can leverage these tools to gain insights into improving the quality of their crops, breaking the cycle of uncertainty and missed opportunities. Traders benefit from the simplified grading process, reducing time and resource inefficiencies associated with transporting samples and enabling more informed decision-making, while roasters gain from greater accuracy in quality assessment, ensuring a consistent and superior final product.
In essence, these emerging AI solutions are poised to revolutionize the way the coffee industry assesses and improves its quality. By providing efficient and objective classification mechanisms, as well as leveraging data-driven insights, these tools offer a path to more streamlined, sustainable and informed processes across the coffee market. As the technology becomes more popular, the anticipated reduction in prices will make these tools more accessible to small producers and small roasters, promoting a more inclusive and innovative scenario for the entire industry.
Anticipating the transformative impact of AI on coffee quality control, below I present some of the AI solutions already available on the market:
Demetria
Demetria, an Israeli-Colombian agricultural technology startup, has developed an AI-powered data intelligence platform that matches each coffee bean profile to the industry-standard coffee flavor wheel. The solution comprises a portable near-infrared (NIR) spectrometer and a machine learning algorithm that assesses the chemical composition of green coffee beans. The spectrometer measures various parameters such as grain size, weight and moisture levels, relaying this information to a mobile application developed by Demetria. The NIR sensors provide a chemical “fingerprint,” which the algorithm maps to the eventual flavor of the coffee post-roasted and prepared in the app. The organized data is then compiled into a quality and traceability data cloud created by collecting insights from Q Graiders.
Best suited for: Traders, consultants, roasters, producers
Type: Hardware and Software
Application focus: Assessment of the flavor and quality of green coffee
Data insights: green coffee flavor assessment, green bean profile, quality measurement, traceability through unique fingerprint.
Csmart
Brazilian agrotechnology startup Csmart specializes in automating specialty coffee processes through cutting-edge technology. Its flagship product, Csmart Digit, employs computer vision, AI and data analytics. The device, equipped with features such as automatic electromagnetic feeder, integrated LED lighting and high-speed CMOS camera, allows users to grade green coffee, extract quality data and generate grading reports. Csmart Digit's AI software provides in-depth analysis, batch evaluations and custom databases, promoting traceability and providing defect prediction. Through continuous flow system integration, Csmart Digit can also evaluate machine efficiency for quality compliance.
Best suited for: Traders, dry grinding facilities, warehouses, roasters and medium to large coffee producers
Type: Hardware and Software
Application focus: Green coffee classification
Data insights: green coffee quality assessment, batch price estimation, batch comparisons, green coffee grading, operational efficiency assessment of machines such as gravity tables, color sorters and ginners.
Agrivero
Agrivero, a Germany-based start-up, recently developed an AI-enabled solution designed for green coffee grading. The process involves loading samples of up to 500 grams of green coffee into the VeroLab device. VeroLab then uses a high-resolution camera, AI and computer vision to individually scan and analyze each green coffee bean from both sides. The results are available in 4 minutes both on the device screen and on the Agrivero Web App, offering analytical insights for efficient decision-making in the green coffee classification process. Users can also use the Agrivero device offline. The company is currently developing an API and integrations into popular ERP systems.
Best suited for: importers, exporters, dry mills, large coffee producers, cooperatives
Type: Hardware and Software
Application focus: Green coffee classification
Data insights: green coffee quality assessment, defect classification, comparison of pre-shipment and arrival samples, traceability through unique sample identification.
ProfilePrint, an AI-based digital food printing platform based in Singapore, uses molecular analysis to predict sensory profiles in green coffee samples. Requiring just 50 grams of coffee beans, the platform scans and generates a comprehensive digital report including cupping scores and sensory parameters. By employing a wide range of light wavelengths, covering both the visible and NIR spectrum, ProfilePrint creates a unique fingerprint for each sample. This spectral data is fed into an AI algorithm, facilitating initial assessment of tasting scores as well as flavor profiles.
Best suited for: importers, exporters, consultants, roasters, producers
Type: Software
Application focus: Green coffee classification
Data insights: green coffee quality assessment, green coffee profile, traceability via unique fingerprint, recommendations on potential blends, fast delivery based on profile.
avercasso, a subsidiary of AVer Information Inc., has ventured into the coffee industry with an AI-driven green coffee sorting machine, the CS One. Inside the machine, a pair of 4K cameras capture multiple images of each green bean while it moves from the hopper to an internal chamber. The machine recognizes primary and secondary defects in green coffee, aligning with the Specialty Coffee Association classifications. The defective beans are propelled by compressed air along a motorized path within the machine to a separate receptacle. The machine features a 10.1-inch touch panel user interface, while a dedicated mobile app provides real-time updates, offering detailed information on the percentage of beans removed due to defects.
Best suited for: Medium to large coffee traders, roasters and producers, consultants
Type: Hardware and Software
Application focus: Green coffee classification
Data insights: good grain recognition, identification of primary and secondary defects classified by SCA, custom generation of green grain database for grain identification and analysis.
These are just a few samples of what technology is coming to the world of coffee.
At Your opinion, is AI more accurate than human assessment? Leave your comment.
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