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

Date Published: April 22, 2019

Publisher: Public Library of Science

Author(s): Ana del-Campo-Sanchez, Rocio Ballesteros, David Hernandez-Lopez, J. Fernando Ortega, Miguel A. Moreno, Lammert Kooistra.


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.

Partial Text

Viticulture is the cornerstone of many rural regions, and grapevines are one of the most important crops grown in France, Spain, Australia, South Africa, and parts of the USA, Chile and Argentina, among other countries. This crop is important not only because of its growth area but also due to its economic impact in rural areas. Therefore, improving crop management is essential for ensuring the sustainability of small holdings as well as the promotion of large wineries in the international market. In Spain, vineyards cover 931,065 ha, which represents 26.6% of the total vineyard surface area in Europe [1]. From 2009 to 2015, wine-producing vineyards in Spain have increased from 39,259,000 hl to 44,415,000 hl, [2]. The high amount of land dedicated to this crop and the progressive increase in production during recent years are the reasons why early detection of agronomic constraints from pests and diseases as well as fertilization and water requirements are some of the main aspects used to improve viticulture management.

The proposed methodology is summarized in Fig 3. A flight was planned based on overlapping values of 60% (forward) and 40% (side). The crop cover presents a convex shape that can be described covered by the overlapping values [44]. An orthoimage was obtained using photogrammetry techniques. In addition, a dense point cloud was generated and segmented into vegetation and ground using geometric techniques, as described below. Following this step, other orthoimages were generated using the dense point cloud that only corresponded to vegetation. Both orthoimages were processed with computer vision techniques for segmenting pest impact pixels from healthy vegetation. The ground truth was obtained from the full orthoimage due to the high resolution of this product. Finally, the percentage of the affected surface was calculated and compared to determine the improvements in the proposed methodology.

Conventional RGB cameras mounted on UAV platforms can be considered a very useful tool for pest aerial detection and quantification. Nevertheless, the enormous amount of information generated as a result of the photogrammetric workflow, i.e., 3D data, may be underused. Most users of UAV platforms are largely focused on the exploitation of 2D geomatic products. However, appropriately processed 3D products, such as accurate and classified points clouds, may improve the accuracy and utility of final applications, such as thematic maps. Compared to the 2D products, the 3D products incorporate the third dimension of a crop (height of the plant and orography), and they demonstrate an improvement in crop health characterization. In addition, incorporating the analysis of 3D information could solve soil distortions derived from remote-sensing techniques.




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