Examinando por Materia "Multispectral imaging"

Mostrando 1 - 3 de 3

Monitoring ryegrass-clover grasslands with multi-spectral UAV imagery will improve the sustainability of small-and medium-sized livestock farmers in the northern Peruvian highlands
(Informa UK Limited, trading as Taylor & Francis Group, 2026-02-04) Vallejos Fernández, Luis; Alvarez García, Wuesley Yusmein; Abanto urbina, Maycol; Gutiérrez Arce, Felipe; Tapia Acosta, Eduardo; Pizarro, Samuel; Ciprian, Cesar; Naupari, Javier
The underutilization of remote sensing technology has compromised sustainable forage resource management, impeding the progress of livestock farmers in the northern Peruvian highlands. To accurately predict forage biomass in six high-altitude (2600-2800 m) ryegrass (Lolium multiflorum Lam) -clover (Trifolium repens) paddocks, we applied machine learning models implemented in Google Earth Engine using spectral indices derived from UAV-based multispectral imagery captured by a Micasense RedEdge MX camera mounted on a DJI Matrice 600. A total of 75 forage samples were collected from precisely geo-referenced plots to train and validate machine learning models based on 13 spectral indices. The Random Forest (RF) model, comprising 500 trees for green forage and dry matter, demonstrated high accuracy and efficiency. UAV-based biomass prediction using GEE and ML techniques was validated, achieving R² values of 0.671 and 0.747 and low errors. By integrating UAVs, sensors, and cloud-based ML, we can decision-support potential in the inter-Andean valley. This innovative approach reduces costs, ensures high-resolution snapshot biomass assessment, and empowers producers to make data-driven decisions.
Rice phenotyping using unmanned aerial vehicles: Analyzing morphological characteristics and yield
(2025-09-26) Goigochea Pinchi, Diego; Vega Herrera, Sergio Sebastian; Torres Chavez, Edson Esmith; Archentti Reategui, Fernando; Barrera Torres, Ciceron; Dominguez Yap, Percy Luis; Ysuiza Perez, Alfredo; Perez Tello, Monica; Rios Rios, Raúl; Santillan Gonzáles, Manuel Dante; Ganoza Roncal, Jorge Juan; Ruiz Reyes, Jose Guillermo; Agurto Piñarreta, Alex Ivan
Rice is a globally important crop and a staple in the diet of a large part of the world's population. This underscores the need for hybridization and improvement of rice genotypes to meet food demand in an environmentally sustainable manner. Geographic Information Systems (GIS) have proven to be valuable tools for the morphometric phenotyping of different genotypes. In this study, seven different rice genotypes were evaluated with the objective of selecting those with high yield. Multispectral imagery was used to develop prediction models based on supervised learning algorithms, including Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Elastic Net (EN), and Neural Networks (NN). The variables studied were plant height, number of panicles, number of tillers, and yield. The results showed the following performances: R² = 0.44 for plant height using Random Forest, R² = 0.92 for number of panicles with Neural Networks, R² = 0.44 for number of tillers with SVM, and R² = 0.31 for yield with SVM. This technology significantly supports traditional selection methodologies for hybridization and improvement by providing a spatial approach that enhances and facilitates selection criteria.
Yield prediction of four bean (Phaseolus vulgaris) cultivars using vegetation indices based on multispectral images from UAV in an arid zone of Peru
(MDPI, 2023-05-19) 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
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.