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Light Drone-Based Application to Assess Soil Tillage Quality Parameters

机译:基于轻型无人机的土壤耕作质量参数评估

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摘要

The evaluation of soil tillage quality parameters, such as cloddiness and surface roughness produced by tillage tools, is based on traditional methods ranging, respectively, from manual or mechanical sieving of ground samples to handheld rulers, non-contact devices or Precision Agriculture technics, such as laser profile meters. The aim of the study was to compare traditional methods of soil roughness and cloddiness assessment (laser profile meter and manual sieving), with light drone RGB 3D imaging techniques for the evaluation of different tillage methods (ploughed, harrowed and grassed). Light drone application was able to replicate the results obtained by the traditional methods, introducing advantages in terms of time, repeatability and analysed surface while reducing the human error during the data collection on the one hand and allowing a labour-intensive field monitoring solution for digital farming on the other. Indeed, the profilometer positioning introduces errors and may lead to false reading due to limited data collection. Future work could be done in order to streamline the data processing operation and so to produce a practical application ready to use and stimulate the adoption of new evaluation indices of soil cloddiness, such as Entropy and the Angular Second Moment (ASM), which seem more suitable than the classic ones to achieved data referred to more extended surfaces.
机译:对土壤耕作质量参数的评估,例如耕作工具产生的衣服的起伏和表面粗糙度,是基于传统方法的,范围分别从手动或机械筛分地面样品到手持式直尺,非接触式设备或精密农业技术,例如作为激光轮廓仪。该研究的目的是比较传统的土壤粗糙度和起伏性评估方法(激光轮廓仪和手动筛分),以及轻型无人机RGB 3D成像技术,用于评估不同耕作方法(耕作,耙地和草皮)。轻型无人机的应用能够复制通过传统方法获得的结果,在时间,可重复性和分析表面方面都具有优势,同时一方面减少了数据收集过程中的人为错误,而且为数字技术提供了劳动密集型的现场监控解决方案另一方面。实际上,轮廓仪的定位会引入错误,并且由于有限的数据收集而可能导致错误的读取。为了简化数据处理操作,可以做进一步的工作,以便产生可立即使用的实用应用程序,并鼓励采用土壤蓬松度的新评估指标,例如熵和角秒矩(ASM),这些指标似乎更多。比经典方法更适合于获得涉及更多扩展表面的数据。

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