Aromatic plants cultivation, processing and marketing is an upcoming agro-industry. The yields from these plants are generally governed by its good management practices of timely, suitable and precise actions against damaging factors. Remote sensing in agriculture is not a new phenomenon anymore, but using unmanned aerial vehicle (UAV, commonly known as drones) for the same is a pertinent topic these days, especially in India. Therefore, the study seeks to perform UAV-based airborne data acquisition, processing and analysis for modernised agricultural practices, finding of which may lead to generate rapid and on-demand real-time remotely sensed data for precision agriculture of commercial crops, which require more care and timely inputs as compared to conventional crops. The UAV high-resolution (1.5 cm/pixel) data were acquired from Mica Sense Altum, a 6 bands multispectral sensor, mounted over an indigenous Quad-copter (< 5 kg). With the help of processed orthoimage, the 22 plots of Rosa damascena (Damask Rose) were precisely (95% accuracy) classified into 03 categories, i.e., rose canopy, weed and open soil areas. We have also estimated digital plant count, plant height derived from canopy height model (CHM), canopy temperature and the topographic conditions of the crop plots. The digital plant counting for R. damascena planted in 4323 m2 area took 1.2 h as compared to manual 5.94 h counting. Average plant height values derived from CHM ranged from 23–68 cm as compared to 28–71 cm manually measured heights. Results were compared with ground sampling data, with which high correlation was found in digital plant count (R2 = 0.99) and plant height (96.69% accuracy). The derived average moderate slopes and northeast aspect suggested suitable topographic conditions required for R. damascena cultivation. The image-derived canopy temperature was compared to the relative ground-based measurements, obtaining accuracy percent of 98.54%. The outcomes are encouraging and have potential to be applied for future UAV grounded applications by farmhands.

中文翻译：

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基于无人机的印度重要商业芳香作物多光谱遥感监测和管理

芳香植物种植、加工和销售是一个即将到来的农业产业。这些植物的产量通常受其针对破坏性因素及时、适当和精确行动的良好管理实践的控制。农业遥感不再是一个新现象，但如今使用无人驾驶飞行器（UAV，通常称为无人机）是一个相关的话题，尤其是在印度。因此，本研究旨在对现代化农业实践进行基于无人机的机载数据采集、处理和分析，其发现可能会为经济作物的精准农业生成快速、按需的实时遥感数据，这需要更多与传统作物相比，护理和及时投入。无人机高分辨率 (1. 5 厘米/像素）数据是从 Mica Sense Altum 获取的，这是一种 6 波段多光谱传感器，安装在本地四轴飞行器（< 5 kg）上。在处理后的正射影像的帮助下，22 个大马士革（Damask Rose）地块被精确地（95% 准确率）分为 03 个类别，即玫瑰冠层、杂草和裸露土壤区域。我们还估计了数字植物数量、来自冠层高度模型 (CHM) 的植物高度、冠层温度和作物地块的地形条件。与手动 5.94 小时计数相比，种植在 4323 平方米区域的大马士革数字植物计数需要 1.2 小时。与手动测量的高度 28-71 厘米相比，源自 CHM 的平均植物高度值范围为 23-68 厘米。结果与地面采样数据进行了比较，在数字植物计数中发现了高度相关性（R2 = 0. 99) 和植物高度 (96.69% 准确度)。推导出的平均中等坡度和东北向表明大马士革大马士革栽培所需的合适地形条件。将图像导出的冠层温度与相对地基测量值进行比较，获得了 98.54% 的准确率。结果令人鼓舞，并有可能被农场工人应用于未来的无人机接地应用。