Unlocking precision horticulture through machine learning-driven 3D canopy analysis
Paper
Paper/Presentation Title | Unlocking precision horticulture through machine learning-driven 3D canopy analysis |
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Presentation Type | Paper |
Authors | Banerjee, B.P., Scalisi, A., Valluri, N., Bonzi, L., O'Connell, M.G., Fitzgerald, G.J. and Goodwin, I. |
Editors | O'Connell, M. |
Journal or Proceedings Title | Acta Horticulturae |
Journal Citation | 1395, pp. 95-102 |
Number of Pages | 8 |
Year | 2024 |
Place of Publication | Australia |
ISSN | 0567-7572 |
ISBN | 9789462613942 |
Digital Object Identifier (DOI) | https://doi.org/10.17660/ActaHortic.2024.1395.13 |
Web Address (URL) of Paper | https://www.actahort.org/books/1395/1395_13.htm |
Web Address (URL) of Conference Proceedings | https://www.actahort.org/books/1395/index.htm |
Conference/Event | II International Symposium on Precision Management of Orchards and Vineyards |
Event Details | II International Symposium on Precision Management of Orchards and Vineyards Delivery Online Event Date 03 to end of 08 Dec 2023 Event Location Tatura, Australia Event Venue Tatura SmartFarms Event Web Address (URL) |
Abstract | Accurate delineation of biomass within orchard ecosystems is imperative for optimising agricultural methodologies, augmenting crop productivity, and streamlining resource allocation. Conventional biomass estimation techniques, predominantly manual in nature, are labour-intensive, time-consuming, and susceptible to error. The advent of remote sensing technologies, notably terrestrial laser scanning, has emerged as a viable alternative for non-invasive and efficient crop monitoring in orchards. This research delineates the application of terrestrial laser scanning in a pear orchard for the precise quantification of foliar and trunk biomass, distinct from fruit yield assessments. The technique facilitates the delineation and quantification of leaf and wood biomass across various pear cultivars. An aspect of this study is using laser scanning to deconstruct tree canopy architecture through skeletonisation processes. This approach yields comprehensive insights into the structural dynamics of the canopy. The precision of data regarding canopy structure is crucial for developing and implementing decision support systems, facilitating improved management practices in irrigation, fertilisation, canopy shaping, and the differential application of agricultural inputs, thereby adhering to the principles of precision horticulture. |
Keywords | canopy geometry; laser scanning; phenotyping; remote sensing; segmentation |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 409901. Agricultural engineering |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Surveying and Built Environment |
Centre for Sustainable Agricultural Systems | |
Agriculture Victoria | |
University of Pisa, Italy | |
University of Melbourne |
https://research.usq.edu.au/item/z7x5q/unlocking-precision-horticulture-through-machine-learning-driven-3d-canopy-analysis
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