Abstract Several vegetation indices have been developed, with the normalized difference vegetation index (NDVI) been the most studied and commonly used. To generate an NDVI map, a relatively high-cost multispectral sensor is required; but currently, most UAVs are equipped with low-cost RGB cameras. For that reason, other indices that utilize RGB data have been developed to generate maps similar to NDVI and minimize the data acquisition cost, such as the triangular greenness index (TGI) and the visible atmospheric resistant index (VARI). However, several studies found that these indices cannot be recommended as reliable general-purpose crop health indicators. This study utilizes a genetic algorithm to develop a new visible index (visible NDVI; vNDVI) that estimates NDVI values of vegetation from uncalibrated RGB cameras mounted on UAVs (or other remote sensing platforms). Three experiments were conducted to create and validate the proposed index. First, the NDVI values generated from a multispectral camera were compared with the NDVI values generated by a hyperspectral camera. In the second experiment, the vNDVI formula was created using a genetic algorithm. The third experiment validates the proposed vNDVI, generated from two uncalibrated RGB cameras, in three different crops (citrus, grapes, and sugarcane). The proposed vNDVI proved to be highly accurate on estimating NDVI values by just using RGB cameras, with an overall mean percentage error of 6.89% and a mean average error of 0.052 in all three crops, providing a low-cost alternative for remote sensing and plant phenotyping.

中文翻译：

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一种新的可见波段指数 (vNDVI)，用于利用遗传算法估计 RGB 图像上的 NDVI 值

摘要 已经发展了多种植被指数，其中归一化差异植被指数（NDVI）是研究最多和最常用的。要生成 NDVI 地图，需要成本相对较高的多光谱传感器；但目前，大多数无人机都配备了低成本的 RGB 摄像头。出于这个原因，已经开发了其他利用 RGB 数据的指数来生成类似于 NDVI 的地图并最大限度地降低数据获取成本，例如三角绿度指数 (TGI) 和可见大气阻力指数 (VARI)。然而，一些研究发现，这些指数不能被推荐为可靠的通用作物健康指标。本研究利用遗传算法开发了一种新的可见指数（visible NDVI；vNDVI)，它通过安装在无人机（或其他遥感平台）上的未校准 RGB 相机估计植被的 NDVI 值。进行了三个实验来创建和验证建议的索引。首先，将多光谱相机生成的 NDVI 值与高光谱相机生成的 NDVI 值进行比较。在第二个实验中，vNDVI 公式是使用遗传算法创建的。第三个实验验证了提议的 vNDVI，该 vNDVI 由两个未校准的 RGB 相机在三种不同的作物（柑橘、葡萄和甘蔗）中生成。事实证明，所提出的 vNDVI 在仅使用 RGB 相机估计 NDVI 值时非常准确，所有三种作物的总体平均百分比误差为 6.89%，平均误差为 0.052，为遥感和植物提供了一种低成本的替代方案表型。