Mapping of Leaf Pigments in Lettuce via Hyperspectral Imaging and Machine Learning

Gonçalves, João Vitor Ferreira; Falcioni, Renan; Rutz, Thiago; Silva, Andre Luiz Biscaia Ribeiro da; Furlanetto, Renato Herrig; Crusiol, Luís Guilherme Teixeira; Oliveira, Karym Mayara de; Oliveira, Caio Almeida de; Vedana, Nicole Ghinzelli; Demattê, José Alexandre Melo; Nanni, Marcos Rafael

doi:10.3390/horticulturae11091077

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Open AccessArticle

Mapping of Leaf Pigments in Lettuce via Hyperspectral Imaging and Machine Learning

by

João Vitor Ferreira Gonçalves

^1,2

,

Renan Falcioni

^2,3,*

,

Thiago Rutz

¹

,

Andre Luiz Biscaia Ribeiro da Silva

^1,*

,

Renato Herrig Furlanetto

⁴

,

Luís Guilherme Teixeira Crusiol

⁵,

Karym Mayara de Oliveira

²

,

Caio Almeida de Oliveira

²,

Nicole Ghinzelli Vedana

²

,

José Alexandre Melo Demattê

⁶

and

Marcos Rafael Nanni

²

¹

Department of Horticulture, Auburn University, Auburn, AL 36849, USA

²

Graduate Program of Agronomy, State University of Maringá, Maringá 87020-900, PR, Brazil

³

Department of Biology, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, PR, Brazil

⁴

Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA

⁵

Embrapa Soja (National Soybean Research Center—Brazilian Agricultural Research Corporation), Londrina 86001-970, PR, Brazil

⁶

Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-260, SP, Brazil

^*

Authors to whom correspondence should be addressed.

Horticulturae 2025, 11(9), 1077; https://doi.org/10.3390/horticulturae11091077 (registering DOI)

Submission received: 10 August 2025 / Revised: 3 September 2025 / Accepted: 3 September 2025 / Published: 5 September 2025

(This article belongs to the Section Vegetable Production Systems)

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Abstract

The nutritional and commercial value of lettuce (Lactuca sativa L.) is determined by its foliar pigment and phenolic composition, which varies among cultivars. This study aimed to assess the capacity of hyperspectral and applied multispectral imaging, combined with machine learning algorithms, to predict and map key biochemical traits, such as chloroplastidic pigments (chlorophylls and carotenoids) and extrachloroplastidic pigments (anthocyanins, flavonoids, and phenolic compounds). Eleven cultivars exhibiting contrasting pigmentation profiles were grown under controlled greenhouse conditions, and their chlorophyll a and b, carotenoid, anthocyanin, flavonoid, and total phenolic contents were evaluated. Spectral reflectance data were acquired via a Headwall hyperspectral sensor and a MicaSense multispectral sensor, and the pigment contents were quantified via solvent extraction and a UV microplate reader. We developed predictive models via seven machine learning approaches, with partial least squares regression (PLSR) and random forest (RF) emerging as the most robust algorithms for pigment estimation. Chlorophyll a and b are highly and positively correlated (r > 0.9), which is consistent with their hyperspectral reflectance imaging results. The hyperspectral data consistently outperformed the multispectral data in terms of predictive accuracy (e.g., R² = 0.91 and 0.76 for anthocyanins and flavonoids via RF) and phenolic compounds with R² = 0.79, capturing subtle spectral features linked to biochemical variation. Spatial maps revealed strong genotype-dependent heterogeneity in pigment and phenolic distributions, supporting the potential of this approach for cultivar discrimination and pigment phenotyping. These findings demonstrate that hyperspectral imaging integrated with data-driven modelling offers a powerful, nondestructive framework for the biochemical monitoring of leafy vegetables, supporting breeding, precision agriculture, and food quality assessment.

Keywords: cultivar discrimination; horticulture; multivariate regression; nondestructive sensing; plant biochemical traits; precision agriculture tools; spectral reflectance modelling

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MDPI and ACS Style

Gonçalves, J.V.F.; Falcioni, R.; Rutz, T.; Silva, A.L.B.R.d.; Furlanetto, R.H.; Crusiol, L.G.T.; Oliveira, K.M.d.; Oliveira, C.A.d.; Vedana, N.G.; Demattê, J.A.M.; et al. Mapping of Leaf Pigments in Lettuce via Hyperspectral Imaging and Machine Learning. Horticulturae 2025, 11, 1077. https://doi.org/10.3390/horticulturae11091077

AMA Style

Gonçalves JVF, Falcioni R, Rutz T, Silva ALBRd, Furlanetto RH, Crusiol LGT, Oliveira KMd, Oliveira CAd, Vedana NG, Demattê JAM, et al. Mapping of Leaf Pigments in Lettuce via Hyperspectral Imaging and Machine Learning. Horticulturae. 2025; 11(9):1077. https://doi.org/10.3390/horticulturae11091077

Chicago/Turabian Style

Gonçalves, João Vitor Ferreira, Renan Falcioni, Thiago Rutz, Andre Luiz Biscaia Ribeiro da Silva, Renato Herrig Furlanetto, Luís Guilherme Teixeira Crusiol, Karym Mayara de Oliveira, Caio Almeida de Oliveira, Nicole Ghinzelli Vedana, José Alexandre Melo Demattê, and et al. 2025. "Mapping of Leaf Pigments in Lettuce via Hyperspectral Imaging and Machine Learning" Horticulturae 11, no. 9: 1077. https://doi.org/10.3390/horticulturae11091077

APA Style

Gonçalves, J. V. F., Falcioni, R., Rutz, T., Silva, A. L. B. R. d., Furlanetto, R. H., Crusiol, L. G. T., Oliveira, K. M. d., Oliveira, C. A. d., Vedana, N. G., Demattê, J. A. M., & Nanni, M. R. (2025). Mapping of Leaf Pigments in Lettuce via Hyperspectral Imaging and Machine Learning. Horticulturae, 11(9), 1077. https://doi.org/10.3390/horticulturae11091077

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Mapping of Leaf Pigments in Lettuce via Hyperspectral Imaging and Machine Learning

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