ABSTRACT
Yield predictions of 'Del Cerro' cotton (Gossypium hirsutum L.) germplasm by multispectral monitoring in the north coast of Peru

Camila Cruz-Grimaldo1, Marite Nieves1, Elvis Vera1, Moises Duran1, Arturo Morales1, Wilian Salazar2, and Carlos I. Arbizu3*
 
Peruvian cotton (Gossypium hirsutum L.) has great acceptance and demand in the national and international textile market due to the excellent quality of its extra-long fiber, durability and resistance. To evaluate cotton cultivar performance, we need to use tools such as drones + sensors. However, these tools have not been widely used in the Peruvian agricultural area. Here we evaluated seven agro-morphological characters of 21 accessions of Del Cerro cotton cultivar from the National Institute of Agrarian Innovation of Peru with high-throughput phenotyping methods. We employed a Matrice 300 RTK unmanned aerial vehicle (UAV) with the MicaSense Dual Red Edge Blue multispectral sensor to assess plant height, yield, and spectral signature during physiological maturity stage; other morphological characters were manually scored. Multispectral monitoring revealed the phytosanitary status of the crop, which begins to enter senescence after 130 d after sowing (DAS) due to the decrease of the vegetation indices (VI). Pearson correlations between yield and VI showed favorable values, exceeding 0.60 at 94 DAS for normalized difference vegetation index (NDVI), relative vigor index (RVI), and normalized difference red edge index (NDRE). Principal component analysis (PCA) was conducted on the same date, a significant correlation was found between NDVI and yield. Additionally, yield prediction equations were generated with the normalized difference water index (NDWI) showing an R value of 0.74 at 130 DAS. The findings of this study suggest that remote sensing evaluation is suitable for estimating ‘Del Cerro’ cotton yield in infrared (IR) bands, providing a tool for germplasm evaluation that can influence decision-making and better conservation strategies.
Keywords: Morphometrics, multivariate analysis, phenomics, UAV, vegetation indices.
1Instituto Nacional de Innovacion Agraria (INIA), Estacion Experimental Agraria Vista Florida, Lambayeque 14301, Peru.
2Instituto Nacional de Innovacion Agraria (INIA), Direccion de Supervision y Monitoreo en Estaciones Experimentales Agraria, Lambayeque 14301, Peru.
3Universidad Nacional Toribio Rodriguez de Mendoza de Amazonas (UNTRM), Facultad de Ingenieria y Ciencias Agrarias, Amazonas 01001, Peru.
*Corresponding author (carlos.arbizu@untrm.edu.pe).