Coffee beverage quality is highly correlated with the degree of fruit ripeness. In this sense, monitoring fruit ripeness is of utmost importance for harvest planning and, especially for obtaining high-quality beverages. Currently, this process is carried out through manual counts of unripe fruits, which is laborious and limited to a few plants within the field. This study aimed at evaluating the potential of a low-cost multispectral camera for coffee ripeness monitoring in the Zona da Mata region of Minas Gerais State, Brazil. For that, five fields of Arabica coffee with distinct characteristics were evaluated. During the coffee ripeness period, four flights were carried using a Phantom 4 Pro quadcopter equipped with a Mapir Survey 3W camera for imagery acquisition. After that, nine vegetation indices (VIs) were obtained. For the same dates, the percentage of unripe fruits was obtained using an irregular grid in all fields. The data was split into two ripeness classes: suitable for harvest (R) with < 30% of unripe fruits; and not suitable for harvest (U), with > 30% of unripe fruits. Then, a principal component analysis was used to infer the importance of the VIs to discriminate plants with unripe fruits from those with ripe fruits. The first two principal components explained > 75% of the variance in the datasets from all coffee fields. The VIs were able to discriminate the ripeness classes (U and R) in most fields; however, their performance was directly influenced by the crop yield and canopy volume.

咖啡饮料的品质与水果的成熟度高度相关。从这个意义上说，监测水果成熟度对于收获计划至关重要，尤其是对于获得高质量的饮料。目前，这个过程是通过人工计数未成熟的水果来进行的，这很费力，而且仅限于田间的少数植物。本研究旨在评估低成本多光谱相机在Zona da Mata咖啡成熟度监测中的潜力巴西米纳斯吉拉斯州地区。为此，对五个具有不同特征的阿拉比卡咖啡领域进行了评估。在咖啡成熟期间，使用配备 Mapir Survey 3W 相机的 Phantom 4 Pro 四轴飞行器进行了四次飞行，用于图像采集。之后，获得了九个植被指数（VI）。对于相同的日期，在所有田地中使用不规则网格获得未成熟果实的百分比。数据分为两个成熟度等级：适合收获 (R)，未成熟果实 < 30%；并且不适合收获 (U)，未成熟的果实 > 30%。然后，使用主成分分析来推断 VI 在区分具有未成熟果实的植物和具有成熟果实的植物方面的重要性。前两个主成分解释> 来自所有咖啡田的数据集中 75% 的方差。VI 能够区分大多数领域的成熟度等级（U 和 R）；然而，它们的表现直接受到作物产量和树冠体积的影响。