Paper Open access published in Remote Sensing on April 10, 2018. Authors: Ana I. de Castro, Francisco M. Jiménez-Brenes, Jorge Torres-Sánchez, José M. Peña, Irene Borra-Serrano and Francisca López-Granados
This study was carried out by researchers from the imaPing group (www.ias.csic.es/imaping) led by Dr. Francisca López-Granados of the Institute of Sustainable Agriculture of Cordoba (IAS-CSIC), the Institute of Agricultural Sciences of Madrid (ICA-CSIC) and the Institute for Agricultural and Fisheries Research of Melle (Belgium).
Precision viticulture has arisen in recent years as a new approach in grape production. It is based on assessing field spatial variability and implementing site-specific management strategies, which can require georeferenced information of the three dimensional (3D) grapevine canopy structure as one of the input data. The 3D structure of vineyard fields can be generated applying photogrammetric techniques to aerial images collected with Unmanned Aerial Vehicles (UAVs), although processing a large amount of crop data embedded in 3D models is currently a bottleneck of this technology.
To solve this limitation, a novel and robust object-based image analysis (OBIA) procedure based on Digital Surface Model (DSM) was developed for 3D grapevine characterization. The significance of this work relies on the developed OBIA algorithm which is fully automatic and self-adaptive to different crop-field conditions, classifying grapevines, and row gap (missing vine plants), and computing vine dimensions without any user intervention.
The results obtained in three testing fields on two different dates showed high accuracy in the classification of grapevine area and row gaps, as well as minor errors in the estimates of grapevine height. In addition, this algorithm computed the position, projected area, and volume of every grapevine in the field. Due to the algorithm output can be exported as geo-referenced maps with the locations and dimensions of every vine, showing the spatial variability of the vineyard, it is a crucial key for precision management. Thereby, the procedure developed, based on ultra-high-spatial resolution DSMs and the OBIA algorithm, has shown to be a valuable tool for the accurate characterization of the vines that has important implications for the adoption of Precision Viticulture. For instance, it could help growers to identify less vigor or size areas that require special attention, monitor the vine growth, determine the proper moment to harvest, or to evaluate the effect different trimming treatments in the grapevine canopy structure.
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