https://www.socictopen.socict.org/files/original/4c695d2e0327c7c64ac3e0a8a1937fe7.pdf 44f24187b234da9629c7d02b262dd2fc Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Agricultura sostenible Description An account of the resource Dominio científico: Agricultura sostenible Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards Creator An entity primarily responsible for making the resource Maite Loidi, Jorge Torres-Sánchez, Francisco Javier Mesas-Carrascosa, Luis-Gonzaga Santesteban, Francisco Manuel Jiménez-Brenes, Oihane Oneka, Ana Villa-Llop, Francisca López-Granados Description An account of the resource Yield prediction is crucial for the management of harvest and scheduling wine production operations. Traditional yield prediction methods rely on manual sampling and are time-consuming, making it difficult to handle the intrinsic spatial variability of vineyards. There have been significant advances in automatic yield estimation in vineyards from on-ground imagery, but terrestrial platforms have some limitations since they can cause soil compaction and have problems on sloping and ploughed land. The analysis of photogrammetric point clouds generated with unmanned aerial vehicles (UAV) imagery has shown its potential in the characterization of woody crops, and the point color analysis has been used for the detection of flowers in almond trees. For these reasons, the main objective of this work was to develop an unsupervised and automated workflow for detection of grape clusters in red grapevine varieties using UAV photogrammetric point clouds and color indices. As leaf occlusion is recognized as a major challenge in fruit detection, the influence of partial leaf removal in the accuracy of the workflow was assessed. UAV flights were performed over two commercial vineyards with different grape varieties in 2019 and 2020, and the photogrammetric point clouds generated from these flights were analyzed using an automatic and unsupervised algorithm developed using free software. The proposed methodology achieved R2 values higher than 0.75 between the harvest weight and the projected area of the points classified as grapes in vines when partial two-sided removal treatment, and an R2 of 0.82 was achieved in one of the datasets for vines with untouched full canopy. The accuracy achieved in grape detection opens the door to yield prediction in red grape vineyards. This would allow the creation of yield estimation maps that will ease the implementation of precision viticulture practices. To the authors’ knowledge, this is the first time that UAV photogrammetric point clouds have been used for grape clusters detection. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource Color thresholding, fruit detection, precision viticulture, remote sensing, unsupervised and automated analysis Identifier An unambiguous reference to the resource within a given context 10.3390/s21093083 Source A related resource from which the described resource is derived Sensors Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chemical technology Relation A related resource <a href="https://www.mdpi.com/1424-8220/21/9/3083" target="_blank" rel="noreferrer noopener">https://www.mdpi.com/1424-8220/21/9/3083</a>