Farmers have more requirements for the completion of cultivations. Remote sensing is a big technology for reducing this requirement. Now, we need an organic spraying system at a low cost. We have two methods, first one neural network algorithm of quantum geographic information system (QGIS) and another one global positioning system (GPS) with drone. This paper describes the analysis of drone remote sensing using the normalized difference vegetation index (NDVI)/Near-infrared band (NIR) sensor in a multispectral view of agricultural land. NIR and NDVI images had water content values and precision values which is mixed in managing water resources. NDVI sensors are loaded to produce high-density images. Real-time monitoring coupled in NIR imaging geometrically and radiometrically adjusted to measure temperature. Multispectral and hyperspectral views had used for analyzing the tested data. Standard irrigation level is 60% to produce the plant growing. Irrigation techniques followed the treatment of the plant within continuous data per second. The implemented view focused only on growth controlling of plant in-depth irrigation between 30 and 90 cm in 60% deviation. NDVI, green normalized difference vegetation index (GNDVI), soil brightness index (SBI), green vegetation index (GVI), degree of yellow vegetation index (YVI), nitrogen sufficiency index (NSI), perpendicular vegetation index (PVI), transformed vegetation index (TVI), soil adjusted vegetation index (SAVI) and vegetation condition index (VCI) vegetation indices are used to the correlation of plant growth control with managing leaf strength and import python packages display the Vegetation various Real-time value in QGIS. Correlation of plant growth p ≤0.01, r = 0.77 and − 0.77 with conductance. It measured degree and demonstrated GPS view using irrigation techniques to control water stress. It had used to estimate the leaf conductance rate with the variation of atmospherically changing. It can calculate real-time leaf stress analysis. This report provided a drone survey analysis of compost percentage and vegetation indices of agricultural land.

中文翻译：

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农业无人机遥感分析

农民对栽培完成的要求更高。遥感是降低这种需求的一项重大技术。现在，我们需要一种低成本的有机喷涂系统。我们有两种方法，一种是量子地理信息系统（QGIS）的神经网络算法，另一种是无人机的全球定位系统（GPS）。本文描述了在农地多光谱视图中使用归一化差异植被指数 (NDVI)/近红外波段 (NIR) 传感器对无人机遥感进行的分析。NIR 和 NDVI 图像具有水含量值和精度值，在水资源管理中混合使用。加载 NDVI 传感器以生成高密度图像。实时监测耦合在近红外成像几何和辐射测量调整以测量温度。多光谱和高光谱视图已用于分析测试数据。标准灌溉水平是 60% 以生产植物生长。灌溉技术在每秒连续数据内跟踪植物的处理。实施的观点仅集中在 60% 偏差的 30 到 90 厘米之间的植物深度灌溉的生长控制。NDVI、绿色归一化植被指数（GNDVI）、土壤亮度指数（SBI）、绿色植被指数（GVI）、黄色植被指数（YVI）、氮充足指数（NSI）、垂直植被指数（PVI）、转化植被指数（TVI），土壤调整植被指数（SAVI）和植被状况指数（VCI）植被指数用于植物生长控制与管理叶片强度的相关性，导入python包在QGIS中显示植被各种实时值。植物生长 p ≤0.01、r = 0.77 和 − 0.77 与电导的相关性。它使用灌溉技术来测量程度并展示 GPS 视图来控制水压力。它曾用于估计叶片电导率随大气变化的变化。它可以计算实时叶片应力分析。本报告提供了农用地堆肥百分比和植被指数的无人机调查分析。它使用灌溉技术来测量程度并展示 GPS 视图来控制水压力。它曾用于估计叶片电导率随大气变化的变化。它可以计算实时叶片应力分析。本报告提供了农用地堆肥百分比和植被指数的无人机调查分析。它使用灌溉技术来测量程度并展示 GPS 视图来控制水压力。它曾用于估计叶片电导率随大气变化的变化。它可以计算实时叶片应力分析。本报告提供了农用地堆肥百分比和植被指数的无人机调查分析。