Yield prediction of four bean (Phaseolus vulgaris) cultivars using vegetation indices based on multispectral images from UAV in an arid zone of Peru

• Autores: Saravia Navarro, David, Valqui Valqui, Lamberto, Salazar Coronal, Wilian, Quille Mamani, Javier Alvaro, Barboza Castillo, Elgar, Porras Jorge, Zenaida Rossana, Injante Silva, Pedro Hugo, Arbizu Berrocal, Carlos Irvin
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:null:20.500.12955/2168
• Enlace del recurso: https://hdl.handle.net/20.500.12955/2168; https://doi.org/10.3390/drones7050325
• Nivel de acceso: acceso abierto
• Materia: Multiple regression; Multispectral imaging; NDVI; Precision agriculture; Remote sensing; https://purl.org/pe-repo/ocde/ford#4.01.06; Multiple regression analysis; Multispectral imagery; Normalized difference vegetation index; Análisis por regresión múltiple; Imágenes multiespectrales; Indice normalizado diferencial de la vegetación; Precision agricultura; Agricultura de precisión; Teledetección

In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, causing a lack of information and precision data on this crop. Here, we predicted the yield of four beans cultivars by using multispectral images, vegetation indices (VIs) and multiple linear correlations (with 11 VIs) in 13 different periods of their phenological development. The multispectral images were analyzed with two methods: (1) a mask of only the crop canopy with supervised classification constructed with QGIS software; and (2) the grids corresponding to each plot (n = 48) without classification. The prediction models can be estimated with higher accuracy when bean plants reached maximum canopy cover (vegetative and reproductive stages), obtaining higher R2 for the c2000 cultivar (0.942) with the CIG, PCB, DVI, EVI and TVI indices with method 2. Similarly, with five VIs, the camanejo cultivar showed the highest R2 for both methods 1 and 2 (0.89 and 0.837) in the reproductive stage. The models better predicted the yield in the phenological stages V3–V4 and R6–R8 for all bean cultivars. This work demonstrated the utility of UAV tools and the use of multispectral images to predict yield before harvest under the Peruvian arid ecosystem.