A First Attempt to Combine NIRS and Plenoptic Cameras for the Assessment of Grasslands Functional Diversity and Species Composition

Simon Taugourdeau, Mathilde Dionisi, Mylène Lascoste, Matthieu Lesnoff, Jean Marie Capron, Fréderic Borne, Philippe Borianne and Lionel Julien
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Simon Taugourdeau: CIRAD UMR SELMET, F-34090 Montpellier, France
Mathilde Dionisi: CIRAD UMR SELMET, F-34090 Montpellier, France
Mylène Lascoste: CIRAD UMR SELMET, F-34090 Montpellier, France
Matthieu Lesnoff: CIRAD UMR SELMET, F-34090 Montpellier, France
Jean Marie Capron: CIRAD UMR SELMET, F-34090 Montpellier, France
Fréderic Borne: CIRAD UMR AMAP, F-34090 Montpellier, France
Philippe Borianne: CIRAD UMR AMAP, F-34090 Montpellier, France
Lionel Julien: CIRAD UMR SELMET, F-34090 Montpellier, France

Agriculture, 2022, vol. 12, issue 5, 1-16

Abstract: Grassland represents more than half of the agricultural land. Numerous metrics (biomass, functional trait, species composition) can be used to describe grassland vegetation and its multiple functions. The measures of these metrics are generally destructive and laborious. Indirect measurements using optical tools are a possible alternative. Some tools have high spatial resolutions (digital camera), and others have high spectral resolutions (Near Infrared Spectrometry NIRS). A plenoptic camera is a multifocal camera that produces clear images at different depths in an image. The objective of this study was to test the interest of combining plenoptic images and NIRS data to characterize different descriptors of two Mediterranean legumes mixtures. On these mixtures, we measured biomass, species biomass, and functional trait diversity. NIRS and plenoptic images were acquired just before the field measurements. The plenoptic images were analyzed using Trainable Weka Segmentation ImageJ to evaluate the percentage of each species in the image. We calculated the average and standard deviation of the different colors (red, green, blue reflectance) in the image. We assessed the percentage of explanation of outputs of the images and NIRS analyses using variance partition and partial least squares. The biomass Trifolium michelianum and Vicia sativa were predicted with more than 50% variability explained. For the other descriptors, the variability explained was lower but nevertheless significant. The percentage variance explained was nevertheless quite low, and further work is required to produce a useable tool, but this work already demonstrates the interest in combining image analysis and NIRS.

Keywords: image segmentation; legumes; Mediterranean grassland; Trifolium; Vicia; Medicago; Avena (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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