Advancements in Coffee Bean Quality Assessment Using Computer Vision and Deep Learning Techniques

Coffee ranks among the most popular drinks consumed worldwide, with millions relying on its stimulating effects and rich flavors The quality of coffee beans is crucial, directly impacting the flavor, aroma, and sensory experience of the brewed coffee. Key factors influencing coffee quality include the variety of the coffee plant, growing conditions, harvesting methods, and post-harvest processing techniques. Traditional quality assessment is labor-intensive and inefficient for large-scale operations, making automation essential. Artificial intelligence, particularly deep learning, has become a vital tool in agriculture and other fields due to its capability to learn and extract complex features automatically. Deep learning, commonly used in image processing, holds great promise for enhancing coffee bean evaluation by analyzing morphological characteristics and providing precise assessments of flavor profiles. This approach can replace manual inspection to identify ripeness, avoid defects, and harmonize key flavor components like sweetness, acidity, and bitterness. This paper reviews the progress of deep learning models in detecting coffee bean quality using computer vision techniques, examining trends and challenges. This paper presents current trends and challenges in detecting coffee bean quality using deep learning and Computer Vision techniques. The detailed study presented in this paper serves as a valuable resource for researchers focused on coffee bean quality assessment. Additionally, several ongoing challenges and issues in coffee quality evaluation are discussed.