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Apple powdery mildew infestation detection and mapping using high-resolution visible and multispectral aerial imaging technique

Type de document
journalArticle
Langue source
-- Langue source --
Titre
Apple powdery mildew infestation detection and mapping using high-resolution visible and multispectral aerial imaging technique
Titre français
Titre anglais
Auteur(s)
  • CHANDEL Abhilash K.
  • KHOT Lav R.
  • C Bernardita Sallato
Editeur(s)
Autre(s)
Id
2D7IFPS8
Version
4120
Url zotero
https://www.zotero.org/groups/2567444/items/2D7IFPS8
Date ajout
25 mai 2021 13:27
Date modification
25 mai 2021 13:27
Résumé
Powdery mildew (PM) in apple orchards is a critical fungal disease that considerably reduces yield, harvested fruit quality, and orchard health. Rapid detection and mapping of resulting infestation at orchard scale is a challenge with existing laborious manual scouting approaches. Therefore, this study explored the feasibility of detecting and mapping PM infestation in an apple orchard block using high-resolution visible (red-green-blue [RGB]) and multispectral imaging technique. Imaging campaigns were conducted over an experimental orchard using small unmanned aerial systems (UAS) integrated with the above optical sensors. K-means classifier trained on individual snapshots of RGB imagery had mean disease detection accuracy of 77%. Eight multispectral vegetation indices also showed significant differences (p < 0.001) between healthy (Mean: 0.25–0.84) and infected (Mean: 0.01–0.25) leaves. Modified Simple Ratio-Red (MSRR), Modified Simple Ratio-Blue (MSRB), and Optimized Soil Adjusted Vegetation Index (OSAVI) showed the highest contrast (Mean: 0.46–0.79). Orchard block-scale mapping using RGB orthomosaic layer, classified with k-means and spectral angle mapper techniques had accuracies up to 73%. Overall, high resolution imagery in the visible and multispectral domain could sufficiently detect the infection and generate site-specific disease heat maps. These maps could be useful to apple growers in directing prescriptive resources ( pruning labor or fungicide application) for PM management.
Note
None
CRAW tags
  • AB - Transversal
  • FREDO lutte
  • FREDO santé végétale
  • FREDO technologie et innovation
  • GEO Etats-Unis
  • arboriculture
  • detection
  • disease
  • mildew
  • pomme
  • powdery mildew
WEB tags
  • apple powdery mildew
  • classification
  • high resolution visible and multispectral imagery
  • infestation heat maps
  • vegetation indices
Titre de la publication
Scientia Horticulturae
Volume
287
Pages
110228
Date caractères
September 20, 2021
Date publication
20 septembre 2021
Doi
10.1016/j.scienta.2021.110228 Le DOI est une URL unique de référencement d'une publication. Il est donc plus fiable et permanent qu'une URL classique
Issn
0304-4238 L’ISSN est un code de 8 chiffres servant à identifier les journaux, revues, magazines, périodiques de toute nature et sur tous supports, papier comme électronique.
Url publication
https://www.sciencedirect.com/science/article/pii/S0304423821003356