1,402
Views
54
CrossRef citations to date
0
Altmetric
Articles

The potential for RGB images obtained using unmanned aerial vehicle to assess and predict yield in sugarcane fields

ORCID Icon, , , , , & show all
Pages 5402-5414 | Received 28 Sep 2017, Accepted 22 Feb 2018, Published online: 13 Mar 2018
 

ABSTRACT

Estimating yield is a major challenge for the majority of agricultural crops. With the advancement of field technologies however, especially those related to the use of Unmanned Aerial Vehicles (UAV) or Drones, the quality of available information has increased, making it possible to overcome technological bottlenecks. However, drone technologies have advanced much faster than studies dealing with the treatment and analysis of information, which can represent an obstacle to the complete adoption of such technologies in sugarcane fields. The objective of the present study was to evaluate the potential for UAV images to assess the degree of canopy closure from different planting approaches and row-spacing treatments applied to sugarcane crop, in order to assess the potential of these tools to predict crop yield. The vegetative growth of the crop was evaluated and the images were obtained at the point of maximum tillering and the inflection point of the biomass accumulation curve. The evaluations included the index; LAI (Leaf Area Index) and GRVI (Green-Red Vegetation Index) obtained by field sensor and UAV, respectively. Because the images from UAV cover the total area, the results revealed that GRVI appears to be much better able to reflect the whole condition of the crop yield (R2 = 0.69) in the field when compared to LAI (R2 = 0.34); demonstrated convincingly by the high spatial resolution capacity of the technology. When integrated, these two indices were able to improve yield estimates by 10% (R2 = 0.79). Images obtained using UAV can represent a low-cost tool for obtaining high-precision remote data that can be used to estimate the agricultural yield of sugarcane fields; and in this way are an effective tool to aid decision making by growers.

Acknowledgments

The authors are grateful to the Iracema Sugar Mill for the field resources provided.

Disclosure statement

No potential conflict of interest was reported by the authors.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 689.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.