UAV remote sensing for phenotyping drought tolerance in peanuts

Balota, Maria; Oakes, Joseph

doi:10.1117/12.2262496

Cited by 15 publications

(17 citation statements)

References 13 publications

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“…The indices a * and u * increased with wilting increase; they became less negative, which indicates that canopy color changed from green to red in stressed and wilted plants. Similar results were found in soybean, wheat, corn, and peanuts confirming our hypothesis that RGB images can discriminate turgid vs. wilted peanut canopies, i.e., tissue turgor pressure falls close to zero under low soil moisture stress (Vergara-Díaz et al, 2015Balota and Oakes, 2017;Zhou et al, 2020).…”

Section: %supporting

confidence: 87%

“…Because, in peanut, wilting is a clear visual symptom of low moisture stress, quantifying wilting is recommended as an important step toward development of cultivars with tolerance to low soil moisture (Luis et al, 2016). Visual rating is an important tool to quantify leaf wilting in plants (Engelbrecht et al, 2007;Hamidou et al, 2012;Luis et al, 2016;Balota and Oakes, 2017;Zhou et al, 2020). Visual rating is based on morphological changes of leaves when cells become less turgid (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models

et al. 2021

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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.

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confidence: 87%

Section: Introductionmentioning

confidence: 99%

Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models

et al. 2021

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“…Wilting of the foliage is a common symptom of peanut plants infected with A. rolfsii [4] and plants under low soil moisture/drought stress [60][61][62]. However, compared to the wilting of the entire plant caused by drought, the typical foliar symptom of peanut plants during the early stage of infection with A. rolfsii is the wilting of an individual lateral branch or main stem [4].…”

Section: Discussionmentioning

confidence: 99%

Identifying Optimal Wavelengths as Disease Signatures Using Hyperspectral Sensor and Machine Learning

et al. 2021

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Hyperspectral sensors combined with machine learning are increasingly utilized in agricultural crop systems for diverse applications, including plant disease detection. This study was designed to identify the most important wavelengths to discriminate between healthy and diseased peanut (Arachis hypogaea L.) plants infected with Athelia rolfsii, the causal agent of peanut stem rot, using in-situ spectroscopy and machine learning. In greenhouse experiments, daily measurements were conducted to inspect disease symptoms visually and to collect spectral reflectance of peanut leaves on lateral stems of plants mock-inoculated and inoculated with A. rolfsii. Spectrum files were categorized into five classes based on foliar wilting symptoms. Five feature selection methods were compared to select the top 10 ranked wavelengths with and without a custom minimum distance of 20 nm. Recursive feature elimination methods outperformed the chi-square and SelectFromModel methods. Adding the minimum distance of 20 nm into the top selected wavelengths improved classification performance. Wavelengths of 501–505, 690–694, 763 and 884 nm were repeatedly selected by two or more feature selection methods. These selected wavelengths can be applied in designing optical sensors for automated stem rot detection in peanut fields. The machine-learning-based methodology can be adapted to identify spectral signatures of disease in other plant-pathogen systems.

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“…While the possibility of using remote‐sensing technology with hyperspectral or multispectral imaging to follow stress has been addressed in some crops, research with peanut is limited. Potential of using reflectance data as an indicator of canopy defoliation (Nutter et al, 1990; Nutter and Littrell, 1996), cultivar and water‐stress discrimination (Balota and Oakes, 2017), and pod maturation (Carley et al, 2008; Rowland et al, 2008) have been reported and more details are needed to establish potential use of remotely‐sensed reflectance data on peanut. In this brief we present data demonstrating differences in red edge reflectance (700–760 nm) and the normalized difference vegetative index (NDVI) when comparing asymptomatic and symptomatic leaves experiencing visual nitrogen deficiency, toxicity from zinc, drought stress, and lesions from disease caused by early leaf spot ( Cercospora arachidicola Hori) and web blotch ( Phoma arachadichola ) in the field during 2004 and 2005.…”

Section: Stress Spectral Classification Red Edge (700–760 Nm) Ndvimentioning

confidence: 99%