Coffee production is a crucial economic, social, and cultural pillar in Latin America, facing numerous challenges, including integrating technological advancements such as multispectral imaging. This approach offers multiple advantages for coffee production; however, a knowledge gap in the domain is the need to methodologically review the available empirical evidence to delineate the field and the study region. Therefore, this systematic mapping aims to map the scientific corpus of multispectral imagery and vegetation index implemented in coffee production in the Latin American region. The study followed the PRISMA protocol; 42 primary studies were analyzed to identify key trends and research gaps. The main result of this research is that NDVI emerged as the most widely used spectral index, with applications in estimating critical biophysical parameters such as biomass and chlorophyll content. Other indices such as GNDVI, NDRE, and SAVI also proved valuable in assessing coffee plant health and development. There was an emerging trend to integrate multispectral imaging with machine learning techniques, promising greater accuracy in data interpretation. The study also revealed a concentration of research efforts in selected Latin American countries, particularly Brazil, indicating opportunities to expand research in other coffee‐producing regions. The study's main conclusion is that multispectral imaging, mainly through vegetation index, has emerged as a valuable tool for phenological monitoring and management of coffee production, offering several advantages over traditional methods. Finally, this review contributes to the existing knowledge base and identifies future research directions for applying multispectral imagery to sustainable coffee production in Latin America.

中文翻译：

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光谱成像和植被指数在拉丁美洲咖啡生产中的应用：系统制图


咖啡生产是拉丁美洲重要的经济、社会和文化支柱，面临着众多挑战，包括整合多光谱成像等技术进步。这种方法为咖啡生产提供了多种优势;然而，该领域的知识差距是需要方法论地审查可用的经验证据以描述该领域和研究区域。因此，本系统制图旨在绘制拉丁美洲地区咖啡生产中实施的多光谱图像和植被指数的科学语料库。该研究遵循 PRISMA 方案;分析了 42 项主要研究以确定主要趋势和研究差距。这项研究的主要结果是 NDVI 成为使用最广泛的光谱指数，可用于估计生物量和叶绿素含量等关键生物物理参数。GNDVI、NDRE 和 SAVI 等其他指数也被证明在评估咖啡植物健康和发育方面也很有价值。将多光谱成像与机器学习技术相结合的新兴趋势有望提高数据解释的准确性。该研究还揭示了研究工作集中在选定的拉丁美洲国家，尤其是巴西，这表明有机会在其他咖啡生产地区扩大研究。该研究的主要结论是，主要通过植被指数进行的多光谱成像已成为咖啡生产物候监测和管理的宝贵工具，与传统方法相比具有多项优势。最后，这篇综述对现有知识库做出了贡献，并确定了将多光谱影像应用于拉丁美洲可持续咖啡生产的未来研究方向。