Characterization of rice yield based on biomass and SPAD-based leaf nitrogen for large genotype plots

Duque Andres F., Patino Diego, Colorado Julian D., Petro Eliel, Rebolledo Maria Camila, Mondragon Ivan F., Espinosa Natalia, Amezquita Nelson, Puentes Oscar D., Mendez Diego, Jaramillo-Botero Andres. 2023. Characterization of rice yield based on biomass and SPAD-based leaf nitrogen for large genotype plots. Sensors, 23 (13), n.spéc. Sensors and Artificial Intelligence in Smart Agriculture:5917, 21 p.

https://doi.org/10.3390/s23135917

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Résumé : The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs.

Mots-clés Agrovoc : biomasse, génotype, télédétection, imagerie multispectrale, rendement des cultures, apprentissage machine, azote, Oryza sativa, riz, méthode statistique, modèle mathématique

Mots-clés géographiques Agrovoc : Colombie

Mots-clés libres : Rice yield, UAV, Multispectral Imagery, Vegetation indices, Machine Learning, Nitrogen and biomass estimation

Classification Agris : F40 - Écologie végétale
F30 - Génétique et amélioration des plantes
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

Agences de financement hors UE : Pontificia Universidad Javeriana, World Bank Group, Colombian Ministry of Science, Technology, and Innovation, Colombian Ministry of Education, Colombian Ministry of Industry and Tourism, Instituto Colombiano de Crédito Educativo y Estudios Técnicos en el Exterior

Projets sur financement : (COL) Optimización Multiescala In-silico de Cultivos Agrícolas Sostenibles

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