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Open AccessArticle

Quantifying Citrus Tree Health Using True Color UAV Images

Kingsville Citrus Center, Texas A&M University, 312 N. International Blvd., Weslaco, TX 78599, USA
Biological and Agricultural Engineering Department, Texas A&M AgriLife Research, Weslaco, TX 78596, USA
Caesar Kleberg Wildlife Research Institute, Texas A&M University-Kingsville, Kingsville, TX 78363, USA
Department of Plant and Soil Sciences, Texas Tech University, Lubbock, TX 79409, USA
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 170;
Received: 26 November 2019 / Revised: 25 December 2019 / Accepted: 31 December 2019 / Published: 3 January 2020
(This article belongs to the Special Issue Remote Sensing for Plant Pathology)
Huanglongbing (HLB) and Phytophthora foot and root rot are diseases that affect citrus production and profitability. The symptoms and physiological changes associated with these diseases are diagnosed through expensive and time-consuming field measurements. Unmanned aerial vehicles (UAVs) using red/green/blue (RGB, true color) imaging, may be an economic alternative to diagnose diseases. A methodology using a UAV with a RGB camera was developed to assess citrus health. The UAV was flown in April 2018 on a grapefruit field infected with HLB and foot rot. Ten trees were selected for each of the following disease classifications: (HLB-, foot rot–), (HLB+, foot rot–), (HLB-, foot rot+) (HLB+, foot rot+). Triangular greenness index (TGI) images were correlated with field measurements such as tree nutritional status, leaf area, SPAD (leaf greenness), foot rot disease severity and HLB. It was found that 61% of the TGI differences could be explained by Na, Fe, foot rot, Ca, and K. This study shows that diseased citrus trees can be monitored using UAVs equipped with RGB cameras, and that TGI can be used to explain subtle differences in tree health caused by multiple diseases. View Full-Text
Keywords: HLB; Phytophthora; UAVs; RGB bands; citrus; TGI HLB; Phytophthora; UAVs; RGB bands; citrus; TGI
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MDPI and ACS Style

Garza, B.N.; Ancona, V.; Enciso, J.; Perotto-Baldivieso, H.L.; Kunta, M.; Simpson, C. Quantifying Citrus Tree Health Using True Color UAV Images. Remote Sens. 2020, 12, 170.

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