Exploring the links between secondary metabolites and leaf spectral reflectance in a diverse genus of Amazonian trees

Fine, Paul V. A.; Salazar, Diego; Martin, Robert E.; Metz, Margaret R.; Misiewicz, Tracy M.; Asner, Gregory P.

doi:10.1002/ecs2.3362

Cited by 17 publications

(8 citation statements)

References 40 publications

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“…In this way, principal component analysis (PCA) clusters have been shown to reflect hyperspectral data and variations in pigment structure components and structure–water bands. Therefore, by utilizing multiple bands and AI algorithms, misclassification problems can be resolved, leading to the production of higher quality vegetables, grains, and fruits as well as correct classification [ 1 , 3 , 4 , 8 , 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms

Falcioni

Gonçalves

Oliveira

et al. 2023

Plants

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In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400–700 nm, 700–1300 nm, and 1300–2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional R2 and ROC values, exceeding 0.99, when compared between all models and confirming the hypothesis and highlighting the potential of AIAs and hyperspectral fingerprints for efficient, precise classification and pigment phenotyping in agriculture. The findings of this study have important implications for the development of efficient methods for phenotyping and classification in agriculture and the potential of AIAs in combination with hyperspectral technology. To advance our understanding of the capabilities of hyperspectroscopy and AIs in precision agriculture and contribute to the development of more effective and sustainable agriculture practices, further research is needed to explore the full potential of these technologies in different crop species and environments.

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Section: Discussionmentioning

confidence: 99%

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms

Falcioni

Gonçalves

Oliveira

et al. 2023

Plants

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“…Subsequently, research teams could assess how communities of microbes, plant‐derived metabolites and plant species detected in feces are influenced by variation in plant species availability at the scale of foraging plots. Remote sensing data, such as hyperspectral aerial images, could then be applied to detect temporal and spatial variation in the composition of plant species and foliar chemistry across the landscape (Fine et al 2021). This type of data collection will require extensive interdisciplinary coordination, but will lead to a more connected understanding of coupled biodiversity among scales.…”

Section: Discussionmentioning

confidence: 99%

Unifying community detection across scales from genomes to landscapes

Hudon

Zaiats

Röser

et al. 2021

Oikos

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To understand how biodiversity responds to global change, we need to connect research across scales, from molecules to landscapes. We show how integrating research disciplines can further a comprehensive understanding of biodiversity, resource-efficient conservation research, and management planning. Using a probabilistic modeling approach, Latent Dirichlet Allocation, we find common features within disparate datasets and present a framework to analyze data about landscape vegetation patterns, plant chemicals, and bacteria in the digestive tracts of sagebrush herbivores. Our study demonstrates how an interdisciplinary approach can aid conservation strategies and how generative models for detecting communities can provide a common language across many types of ecological data. SynthesisBiodiversity science encompasses multiple disciplines and biological scales from molecules to landscapes. Nevertheless, biodiversity data are often analyzed separately with discipline-specific methodologies, constraining resulting inferences to a single scale. To overcome this, we present a topic modeling framework to analyze community composition in cross-disciplinary datasets, including those generated from metagenomics, metabolomics, field ecology and remote sensing. Using topic models, we demonstrate how community detection in different datasets can inform the conservation of interacting plants and herbivores. We show how topic models can identify members of molecular, organismal and landscape-level communities that relate to wildlife health, from gut microbes to forage quality. We conclude with a future vision for how topic modeling can be used to design cross-scale studies that promote a holistic approach to detect, monitor and manage biodiversity.

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“…This highlights the fact that to exploit hyperspectral data, more techniques need to be explored to process the data. The correlation heat map as per [4] may indicate the metabolite signature on the spectra, but to make those data useful information, more techniques need to be implemented. The absorption indices that show a significant correlation and a magnitude value within the range, as compared to the measured TC, are listed in Table 2.…”

Section: Comparative Analysis Between Indices Selected Wavelengths An...mentioning

confidence: 99%

“…The major determining components of vegetation include biochemical constituents that are central to their physiological form and function, along with water, chlorophyll, and accessory pigments, nitrogen, cellulose, starch, sugars, lignin, and protein. These are the mandatory parameters for describing the nutritional status of any tree of a particular ecosystem [2,3], while the secondary metabolites such as terpenes, sesquiterpenes, phytosterols, etc., are more useful to humans [4], which makes the plant economically valuable.…”

Section: Introductionmentioning

confidence: 99%

Statistical Unfolding Approach to Understand Influencing Factors for Taxol Content Variation in High Altitude Himalayan Region

Gupta

Srivastava

Petropoulos

et al. 2021

Forests

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Taxol drugs can be extracted from various species of the taxaceae family. It is an alkaloid (metabolic product) used for the treatment of various types of cancer. Since taxol is a metabolic product, multiple aspects such as edaphic, biochemical, topographic factors need to be assessed in determining the variation in Taxol Content (TC). In this study, both sensor-based hyperspectral reflectance data and absorption-based indices were tested together for the development of an advanced statistical unfolding approach to understand the influencing factors for TC in high altitude Himalayan region. Seriation analysis based on permutation matrix was applied with complete linkage and a multi-fragment heuristic scaling rule along with the common techniques such as Principal Component Analysis (PCA) and correlation to understand the relationship of TC with various factors. This study also tested the newly developed taxol indices to rule out the possibility of overlapping of TC determining bands with the foliar pigment’s wavelengths in the visible region. The result implies that T. wallichiana with a high TC is found more in its natural habitat of deep forest, relating it indirectly to elevation in the case of the montane ecosystem. Taxol is the most varying parameter among the measured variables, followed by hyperspectral Taxol content (TC) indices such as TC 2, TC 5, and carotenoids, which suggests that the indices are well versed to capture variations in TC with elevation.

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Exploring the links between secondary metabolites and leaf spectral reflectance in a diverse genus of Amazonian trees

Abstract: Exploring the links between secondary metabolites and leaf spectral reflectance in a diverse genus of Amazonian trees. Ecosphere 12(2): e03362.

Cited by 17 publications

References 40 publications

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms

Unifying community detection across scales from genomes to landscapes

Statistical Unfolding Approach to Understand Influencing Factors for Taxol Content Variation in High Altitude Himalayan Region

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