Solano-Alvarez Nallely, Valencia-Hernández Juan Antonio, Vergara-Pineda Santiago, Millán-Almaraz Jesús Roberto, Torres-Pacheco Irineo, Guevara-González Ramón Gerardo
C.A. Ingeniería de Biosistemas, Facultad de Ingeniería, Campus Amazcala, Universidad Autónoma de Querétaro, Carretera Chichimequillas s/n km1, El Marques 76265, Querétaro, Mexico.
Ingeniería Agroquímica, Facultad de Química, Universidad Autónoma de Querétaro, Cerro de las Campanas s/n Col. las Campanas, Santiago de Querétaro 76010, Querétaro, Mexico.
Plants (Basel). 2022 Mar 30;11(7):932. doi: 10.3390/plants11070932.
Precision agriculture has the objective of improving agricultural yields and minimizing costs by assisting management with the use of sensors, remote sensing, and information technologies. There are several approaches to improving crop yields where remote sensing has proven to be an important methodology to determine agricultural maps to show surface differences which may be associated with many phenomena. Remote sensing utilizes a wide variety of image sensors that range from common RGB cameras to sophisticated, hyper-spectral image cameras which acquire images from outside the visible electromagnetic spectrum. The NDVI and NGBVI are computer vision vegetation index algorithms that perform operations from color masks such as red, green, and blue from RGB cameras and hyper-spectral masks such as near-infrared (NIR) to highlight surface differences in the image to detect crop anomalies. The aim of the present study was to determine the relationship of NDVI and NGBVI as plant health indicators in tomato plants () treated with the beneficial bacteria -Amazcala (-A) as a protective agent to cope with () infections. The results showed that in the presence of -A after infection with , NDVI and NGBVI can be used as markers of plant weight and the activation of the enzymatic activities related to plant defense induction.
精准农业的目标是通过利用传感器、遥感和信息技术辅助管理，提高农业产量并将成本降至最低。提高作物产量有多种方法，其中遥感已被证明是确定农业地图以显示可能与多种现象相关的地表差异的重要方法。遥感利用各种各样的图像传感器，从普通的RGB相机到复杂的高光谱图像相机，这些相机从可见电磁光谱之外获取图像。归一化植被指数（NDVI）和新型绿度植被指数（NGBVI）是计算机视觉植被指数算法，它们根据RGB相机的红色、绿色和蓝色等颜色掩码以及近红外（NIR）等高光谱掩码进行操作，以突出图像中的地表差异，检测作物异常。本研究的目的是确定作为植物健康指标的NDVI和NGBVI与用有益细菌阿马兹卡拉（-A）处理作为保护剂以应对（）感染的番茄植株（）之间的关系。结果表明，在感染后存在 -A的情况下，NDVI和NGBVI可作为植物重量以及与植物防御诱导相关的酶活性激活的标志物。
