Deep Learning for Coffee Leaf Disease Detection: Opportunities and Challenges for Quality Traceability in Agricultural E-Commerce

Coffee leaf diseases impair photosynthesis, thereby degrading the chemical composition and flavor quality of coffee beans. However, these resulting quality defects are often not visible from the appearance of green beans alone, compelling e-commerce quality control to trace back to leaf disease detection at the production origin. This focus on “quality traceability” imposes requirements on computer vision technologies that fundamentally differ from those of conventional pesticide-application-oriented detection: prioritizing high precision over high recall, replacing simple classification with severity grading, and necessitating the integration of variety and origin metadata. This paper conducts a systematic review of 53 relevant studies published from January 2020 to March 2026, examining existing data resources, model architectures, and industrial adaptability through the lens of e-commerce quality traceability. Our review highlights three major findings: (1) existing datasets could be further enriched in variety labeling, severity scoring, and origin metadata; (2) current models, predominantly focused on classification, have room for closer alignment with traceability requirements regarding optimization objectives, task definitions, and output formats; and (3) cutting-edge technologies, including semantic segmentation, multimodal fusion, and visual foundation models, offer viable pathways to bridge these gaps. This review provides standardized technical evaluation criteria and clear optimization directions for origin inspection, batch grading and whole-chain traceability management of coffee agricultural e-commerce platforms.