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. 2019 Apr 22;14(4):e0215521.
doi: 10.1371/journal.pone.0215521. eCollection 2019.

Quantifying the effect of Jacobiasca lybica pest on vineyards with UAVs by combining geometric and computer vision techniques

Ana Del-Campo-Sanchez  1 , Rocio Ballesteros  1 , David Hernandez-Lopez  1 , J Fernando Ortega  1 , Miguel A Moreno  1 ; Agroforestry and Cartography Precision Research Group
Affiliations

Quantifying the effect of Jacobiasca lybica pest on vineyards with UAVs by combining geometric and computer vision techniques

Ana Del-Campo-Sanchez et al. PLoS One. .

Abstract

With the increasing competitiveness in the vine market, coupled with the increasing need for sustainable use of resources, strategies for improving farm management are essential. One such effective strategy is the implementation of precision agriculture techniques. Using photogrammetric techniques, the digitalization of farms based on images acquired from unmanned aerial vehicles (UAVs) provides information that can assist in the improvement of farm management and decision-making processes. The objective of the present work is to quantify the impact of the pest Jacobiasca lybica on vineyards and to develop representative cartography of the severity of the infestation. To accomplish this work, computational vision algorithms based on an ANN (artificial neural network) combined with geometric techniques were applied to geomatic products using consumer-grade cameras in the visible spectra. The results showed that the combination of geometric and computational vision techniques with geomatic products generated from conventional RGB (red, green, blue) images improved image segmentation of the affected vegetation, healthy vegetation and ground. Thus, the proposed methodology using low-cost cameras is a more cost-effective application of UAVs compared with multispectral cameras. Moreover, the proposed method increases the accuracy of determining the impact of pests by eliminating the soil effects.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Location of the case study plot.
Source: Spanish National Plan of Aerial Orthophotography.
Fig 2
Fig 2. UAV utilizing a mounted SONY α ILCE-5100L sensor + E 20 mm F2.8 lens.
Fig 3
Fig 3. Flowchart of the proposed methodology.
GSD: Ground Sample Distance.
Fig 4
Fig 4. Appearance of healthy and affected leaves.
Fig 5
Fig 5. Footprint of the images for the flight planning process.
Fig 6
Fig 6. Flowchart of LAIC software.
Fig 7
Fig 7
Orthoimage (left) and impact detected with computer vision techniques (right).
Fig 8
Fig 8. 3D view of classified point cloud.
Brown: ground. Green: medium vegetation. Pink: noise.
Fig 9
Fig 9
Orthoimage without ground (left) and impact detected in a nonground orthoimage (right).
Fig 10
Fig 10
Maps of independent overall accuracies (up), independent commission error over the total considered pixels (center) and independent omission error over the total considered pixels (down) committed for both methodologies (full orthoimage left, nonground orthoimage right).
Fig 11
Fig 11
Percentage and histogram of impact detected on 1 m2 for the full (left) and nonground (right) orthoimages.
Fig 12
Fig 12
Maps of canopy cover (left) and the percentage of impact with the proposed methodology (right).
See this image and copyright information in PMC

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Publication types

Grants and funding

This project is supported by a 2017 Leonardo Grant for Researchers and Cultural Creators, the BBVA Foundation and project AGL2014-59747-C2-1-R (Co-funded by FEDER) of the Spanish Ministry of Education and Science (MEC).
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