Artificial intelligence and multispectral imaging in coffee production: A systematic literature review

Abstract

Integrating multispectral imaging and artificial intelligence in coffee production is a promising approach to optimize farming practices and improve crop management. This systematic literature review analyzes the current status, challenges, and future directions for combining these technologies in the coffee sector. Following the PRISMA protocol, 455 papers were reviewed in six scientific databases, identifying 27 primary studies that met the inclusion and exclusion criteria. The analysis reveals a significant increase in research activity since 2020, with a relationship between time and frequency of publication. Machine learning techniques, particularly regression analysis and random forests, emerged as the predominant artificial intelligence approaches for multispectral data processing. The review identified several key applications, such as coffee quality assessment, disease detection, and yield prediction. However, significant challenges remain, such as limited biometric variability within coffee plants, the influence of environmental factors, and the need for high-quality training data. The effectiveness of these technologies varies across geographic regions and soil and climatic conditions, underscoring the importance of application in specific contexts. Future research points toward the development of more robust artificial intelligence models, the integration of multiple data sources, and the need to employ hybrid artificial intelligence approaches. This review provides an understanding of the current landscape and valuable information for researchers, industry professionals, and stakeholders interested in creating more efficient and sustainable coffee farming practices using these technologies.

Plant phenotyping relevance

コーヒー生産におけるマルチスペクトル画像とAIの植物状態・形質推定を扱う系統的レビューであり、フェノタイピング関連手法のレビューが中心である。

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.