An Improved Crop Scouting Technique Incorporating Unmanned Aerial Vehicle-Assisted Multispectral Crop Imaging into Conventional Scouting Practice for Gummy Stem Blight in Watermelon

Melanie Kalischuk, Mathews L. Paret, Joshua H. Freeman, Darren Raj, Susannah Da Silva, Shep Eubanks, D. J. Wiggins, Matthew Lollar, James J. Marois, H. Charles Mellinger, Jnaneshwar Das

Research output: Contribution to journalArticle

Abstract

Multispectral imaging is increasingly used in specialty crops, but its benefits in assessment of disease severity and improvements in conventional scouting practice are unknown. Multispectral imaging was conducted using an unmanned aerial vehicle (UAV), and data were analyzed for five flights from Florida and Georgia commercial watermelon fields in 2017. The fields were rated for disease incidence and severity by extension agents and plant pathologists at randomized locations (i.e., conventional scouting) followed by ratings at locations that were identified by differences in normalized difference vegetation index (NDVI) and stress index (i.e., UAV-assisted scouting). Diseases identified by the scouts included gummy stem blight, anthracnose, Fusarium wilt, Phytophthora fruit rot, Alternaria leaf spot, and cucurbit leaf crumple disease. Disease incidence and severity ratings were significantly different between conventional and UAV-assisted scouting (P < 0.01, Bhapkar/exact test). Higher severity ratings of 4 and 5 on a scale of 1 to 5 from no disease to complete loss of the canopy were more consistent after the scouts used the multispectral images in determining sampling locations. The UAV-assisted scouting locations had significantly lower green, red, and red edge NDVI values and higher stress index values than the conventional scouting areas (P < 0.05, ANOVA/Tukey), and this corresponded to areas with higher disease severity. Conventional scouting involving human evaluation remains necessary for disease validation. Multispectral imagery improved watermelon field scouting owing to increased ability to identify disease foci and areas of concern more rapidly than conventional scouting practices with early detection of diseases 20% more often using UAV-assisted scouting.

Original languageEnglish (US)
Pages (from-to)1642-1650
Number of pages9
JournalPlant disease
Volume103
Issue number7
DOIs
StatePublished - Jul 1 2019

Fingerprint

watermelons
blight
disease severity
image analysis
stems
crops
disease incidence
leaf spot
multispectral imagery
specialty crops
extension agents
disease detection
methodology
Fusarium wilt
anthracnose
Cucurbitaceae
Alternaria
Phytophthora
flight
analysis of variance

Keywords

  • cucurbits
  • drone-assisted scouting
  • precision agriculture
  • UAV
  • watermelon

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Plant Science

Cite this

An Improved Crop Scouting Technique Incorporating Unmanned Aerial Vehicle-Assisted Multispectral Crop Imaging into Conventional Scouting Practice for Gummy Stem Blight in Watermelon. / Kalischuk, Melanie; Paret, Mathews L.; Freeman, Joshua H.; Raj, Darren; Da Silva, Susannah; Eubanks, Shep; Wiggins, D. J.; Lollar, Matthew; Marois, James J.; Mellinger, H. Charles; Das, Jnaneshwar.

In: Plant disease, Vol. 103, No. 7, 01.07.2019, p. 1642-1650.

Research output: Contribution to journalArticle

Kalischuk, M, Paret, ML, Freeman, JH, Raj, D, Da Silva, S, Eubanks, S, Wiggins, DJ, Lollar, M, Marois, JJ, Mellinger, HC & Das, J 2019, 'An Improved Crop Scouting Technique Incorporating Unmanned Aerial Vehicle-Assisted Multispectral Crop Imaging into Conventional Scouting Practice for Gummy Stem Blight in Watermelon', Plant disease, vol. 103, no. 7, pp. 1642-1650. https://doi.org/10.1094/PDIS-08-18-1373-RE
Kalischuk, Melanie ; Paret, Mathews L. ; Freeman, Joshua H. ; Raj, Darren ; Da Silva, Susannah ; Eubanks, Shep ; Wiggins, D. J. ; Lollar, Matthew ; Marois, James J. ; Mellinger, H. Charles ; Das, Jnaneshwar. / An Improved Crop Scouting Technique Incorporating Unmanned Aerial Vehicle-Assisted Multispectral Crop Imaging into Conventional Scouting Practice for Gummy Stem Blight in Watermelon. In: Plant disease. 2019 ; Vol. 103, No. 7. pp. 1642-1650.
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abstract = "Multispectral imaging is increasingly used in specialty crops, but its benefits in assessment of disease severity and improvements in conventional scouting practice are unknown. Multispectral imaging was conducted using an unmanned aerial vehicle (UAV), and data were analyzed for five flights from Florida and Georgia commercial watermelon fields in 2017. The fields were rated for disease incidence and severity by extension agents and plant pathologists at randomized locations (i.e., conventional scouting) followed by ratings at locations that were identified by differences in normalized difference vegetation index (NDVI) and stress index (i.e., UAV-assisted scouting). Diseases identified by the scouts included gummy stem blight, anthracnose, Fusarium wilt, Phytophthora fruit rot, Alternaria leaf spot, and cucurbit leaf crumple disease. Disease incidence and severity ratings were significantly different between conventional and UAV-assisted scouting (P < 0.01, Bhapkar/exact test). Higher severity ratings of 4 and 5 on a scale of 1 to 5 from no disease to complete loss of the canopy were more consistent after the scouts used the multispectral images in determining sampling locations. The UAV-assisted scouting locations had significantly lower green, red, and red edge NDVI values and higher stress index values than the conventional scouting areas (P < 0.05, ANOVA/Tukey), and this corresponded to areas with higher disease severity. Conventional scouting involving human evaluation remains necessary for disease validation. Multispectral imagery improved watermelon field scouting owing to increased ability to identify disease foci and areas of concern more rapidly than conventional scouting practices with early detection of diseases 20{\%} more often using UAV-assisted scouting.",
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