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dc.date.accessioned 2018-11-27T17:39:49Z
dc.date.available 2018-11-27T17:39:49Z
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/70990
dc.description.abstract Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection. en
dc.language en es
dc.subject unmanned aerial system en
dc.subject supervised learning en
dc.subject corn en
dc.subject farm management en
dc.subject precision agriculture en
dc.title Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques en
dc.type Objeto de conferencia es
sedici.identifier.uri http://47jaiio.sadio.org.ar/sites/default/files/CAI-9.pdf es
sedici.identifier.issn 2525-0949 es
sedici.creator.person Varela, Sebastián es
sedici.creator.person Dhodda, Pruthvidhar Reddy es
sedici.creator.person Hsu, William H. es
sedici.creator.person Vara Prasad, P. V. es
sedici.creator.person Assefa, Yared es
sedici.creator.person Peralta, Nahuel R. es
sedici.creator.person Griffin, Terry es
sedici.creator.person Sharda, Ajay es
sedici.creator.person Ferguson, Allison es
sedici.creator.person Ciampitti, Ignacio A. es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Sociedad Argentina de Informática e Investigación Operativa es
sedici.subtype Resumen es
sedici.rights.license Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-sa/3.0/
sedici.date.exposure 2018-09
sedici.relation.event X Congreso de AgroInformática (CAI) - JAIIO 47 (CABA, 2018) es
sedici.description.peerReview peer-review es
sedici.relation.isRelatedWith https://doi.org/10.3390/rs10020343 es


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Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)