Literature DB >> 31082305

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

Melanie Kalischuk1, Mathews L Paret1,2, Joshua H Freeman1,3, Darren Raj4, Susannah Da Silva1, Shep Eubanks5, D J Wiggins5, Matthew Lollar6, James J Marois7, H Charles Mellinger7, Jnaneshwar Das8.   

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.

Entities:  

Keywords:  UAV; cucurbits; drone-assisted scouting; precision agriculture; watermelon

Mesh:

Year:  2019        PMID: 31082305     DOI: 10.1094/PDIS-08-18-1373-RE

Source DB:  PubMed          Journal:  Plant Dis        ISSN: 0191-2917            Impact factor:   4.438


  1 in total

1.  Identification and Classification of Downy Mildew Severity Stages in Watermelon Utilizing Aerial and Ground Remote Sensing and Machine Learning.

Authors:  Jaafar Abdulridha; Yiannis Ampatzidis; Jawwad Qureshi; Pamela Roberts
Journal:  Front Plant Sci       Date:  2022-05-20       Impact factor: 6.627

  1 in total

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