Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models

Peanut ( Arachis hypogaea L.) is an important crop for United States agriculture and worldwide. Low soil moisture is a major constraint for production in all peanut growing regions with negative effects on yield quantity and quality. Leaf wilting is a visual symptom of low moisture stress used in br...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Frontiers in plant science Ročník 12; s. 658621
Hlavní autoři:	Sarkar, Sayantan, Ramsey, A. Ford, Cazenave, Alexandre-Brice, Balota, Maria
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland Frontiers Media SA 18.06.2021 Frontiers Media S.A
Témata:	Agricultural production Biomass Breeding Color imagery Corn Crops Digital cameras Drought high-throughput phenotyping Irrigation Leaves Legumes logistic regression Logit models machine learning Moisture stress peanut leaf wilting Peanuts Photogrammetry Plant Science Precipitation Regression models Remote sensing RGB color space indices Saturation (color) Seeds Senescence Soil moisture Soil stresses Sorghum Unmanned aerial vehicles Vegetation Vegetation index Water Water shortages Wilting United States > US Virginia peanut leaf wilting RGB color space indices logistic regression machine learning high-throughput phenotyping
ISSN:	1664-462X, 1664-462X
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	Peanut ( Arachis hypogaea L.) is an important crop for United States agriculture and worldwide. Low soil moisture is a major constraint for production in all peanut growing regions with negative effects on yield quantity and quality. Leaf wilting is a visual symptom of low moisture stress used in breeding to improve stress tolerance, but visual rating is slow when thousands of breeding lines are evaluated and can be subject to personnel scoring bias. Photogrammetry might be used instead. The objective of this article is to determine if color space indices derived from red-green-blue (RGB) images can accurately estimate leaf wilting for breeding selection and irrigation triggering in peanut production. RGB images were collected with a digital camera proximally and aerially by a unmanned aerial vehicle during 2018 and 2019. Visual rating was performed on the same days as image collection. Vegetation indices were intensity, hue, saturation, lightness, a ∗ , b ∗ , u ∗ , v ∗ , green area (GA), greener area (GGA), and crop senescence index (CSI). In particular, hue, a ∗ , u ∗ , GA, GGA, and CSI were significantly ( p ≤ 0.0001) associated with leaf wilting. These indices were further used to train an ordinal logistic regression model for wilting estimation. This model had 90% accuracy when images were taken aerially and 99% when images were taken proximally. This article reports on a simple yet key aspect of peanut screening for tolerance to low soil moisture stress and uses novel, fast, cost-effective, and accurate RGB-derived models to estimate leaf wilting.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science Edited by: Shawn Carlisle Kefauver, University of Barcelona, Spain Reviewed by: Jill E. Cairns, International Maize and Wheat Improvement Center, Mexico; Tom De Swaef, Institute for Agricultural, Fisheries and Food Research (ILVO), Belgium; Jiale Jiang, King Abdullah University of Science and Technology, Saudi Arabia
ISSN:	1664-462X 1664-462X
DOI:	10.3389/fpls.2021.658621