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Digital monitoring of agro-ecosystems indicators on the basis of space and unmanned technologies

机译:在空间和无人技术的基础上,农业生态系统指标的数字监测

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摘要

The aim of the research is to develop a digital monitoring methodology for agro-ecosystem indicators based on space and unmanned technologies with the conversion of digital aerial photography results into real parameters of agrophytocenosis indicators as physical units. The paper uses the results of research on digital monitoring of agrophytocenosis of single- and poly-species crops of grain and leguminous crops on the experimental field of the Samara State Agrarian University in 2018 (southern forest-steppe of Trans-Volga region). The soil is a typical medium heavy black loamy chernozem (humus content is 5.9 %, easily hydrolysed nitrogen is 80–120 mg/kg, labile phosphorus is 135–145 mg/kg, exchangeable potassium is 150–195 mg/kg, salt extract pH is 6.9). The research conducted in 2018 revealed that the stated aim is achievable in principle. According to the results of 2018, the greatest correlation between the vegetative biomass index NDVI and grain yield for winter and spring cereal crops was detected at the milky stage of the grains (up to r = 0.82).
机译:该研究的目的是基于空间和无人技术的农业生态系统指标为数字鸟瞰摄影结果转换为物理单位,为物理单位的实际参数进行了基于空间和无人技术的数字监测方法。本文采用了2018年撒玛拉州农作物实验领域的粮食和豆科作物的单一和多种粮食作物胚胎和聚类作物的数字监测研究结果(Trans-Volga地区的Southern Forest-Steppe)。土壤是一种典型的介质重型黑色荡裂性Chernozem(腐殖质含量为5.9%,易水解的氮气为80-120 mg / kg,不稳定磷是135-145mg / kg,可更换钾是150-195mg / kg,盐提取物pH是6.9)。 2018年进行的该研究表明,规定的目的是原则上可实现的。根据2018年的结果,在晶粒的乳状阶段检测到冬季冬季和春季谷物作物的营养生物量指数NDVI与谷物产量之间的最大相关性(直至r = 0.82)。

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