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基于轻型无人机的土壤耕作质量参数评估应用。

Light Drone-Based Application to Assess Soil Tillage Quality Parameters.

机构信息

Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria (CREA) - Centro di ricerca Ingegneria e Trasformazioni Agroalimentari, Via della Pascolare 16, 00015 Monterotondo (Rome), Italy.

出版信息

Sensors (Basel). 2020 Jan 28;20(3):728. doi: 10.3390/s20030728.

Abstract

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.

摘要

土壤耕作质量参数的评估,如耕作工具产生的土块和表面粗糙度,是基于传统的方法,分别从手动或机械筛分地面样本到手持尺子、非接触式设备或精准农业技术,如激光剖面仪。本研究的目的是比较土壤粗糙度和土块评估的传统方法(激光剖面仪和手动筛分)与轻型无人机 RGB 3D 成像技术,用于评估不同的耕作方法(耕、耙和种草)。轻型无人机的应用能够复制传统方法的结果,在时间、重复性和分析表面方面引入了优势,同时减少了数据收集过程中的人为错误,另一方面允许对数字农业进行劳动密集型的现场监测。事实上,剖面仪的定位会引入误差,并可能导致错误的读数,因为数据采集有限。未来的工作可以简化数据处理操作,以便生成一个实用的应用程序,随时可用,并鼓励采用新的土壤土块评估指数,如熵和角二阶矩(ASM),它们似乎比经典指数更适合于获得更多扩展表面的参考数据。

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