Source: The Plant Phenome Journal. Unidade: ESALQ
Subjects: AERONAVES NÃO TRIPULADAS, ANÁLISE ESPECTRAL, ANTRACNOSE, FENOLOGIA, FENÓTIPOS, FUNGOS FITOPATOGÊNICOS, IMAGEM DIGITAL, SORGO
ABNT
GALLI, Giovanni et al. Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum. The Plant Phenome Journal, v. 3, p. 1-14, 2020Tradução . . Disponível em: https://doi.org/10.1002/ppj2.20010. Acesso em: 17 nov. 2024.APA
Galli, G., Horne, D. W., Collins, S. D., Jung, J., Chang, A., Fritsche‐Neto, R., & Rooney, W. L. (2020). Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum. The Plant Phenome Journal, 3, 1-14. doi:10.1002/ppj2.20010NLM
Galli G, Horne DW, Collins SD, Jung J, Chang A, Fritsche‐Neto R, Rooney WL. Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum [Internet]. The Plant Phenome Journal. 2020 ; 3 1-14.[citado 2024 nov. 17 ] Available from: https://doi.org/10.1002/ppj2.20010Vancouver
Galli G, Horne DW, Collins SD, Jung J, Chang A, Fritsche‐Neto R, Rooney WL. Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum [Internet]. The Plant Phenome Journal. 2020 ; 3 1-14.[citado 2024 nov. 17 ] Available from: https://doi.org/10.1002/ppj2.20010