| Full text | |
| Author(s): |
Wellington Renato Mancin
[1]
;
Lilian Elgalise Techio Pereira
[2]
;
Rachel Santos Bueno Carvalho
[3]
;
Yeyin Shi
[4]
;
Wilson Manuel Castro Silupu
[5]
;
Adriano Rogério Bruno Tech
[6]
Total Authors: 6
|
| Affiliation: | [1] Departamento de Ciências Básicas (ZAB). Faculdade de Zootecnia e Engenharia de Alimentos (FZEA/USP) - Brasil
[2] Departamento de Zootecnia (ZAZ). Faculdade de Zootecnia e Engenharia de Alimentos (FZEA/USP) - Brasil
[3] Departamento de Ciências Básicas (ZAB). Faculdade de Zootecnia e Engenharia de Alimentos (FZEA/USP) - Brasil
[4] University of Nebraska-Lincoln. Department of Biological Systems Engineering - Estados Unidos
[5] Universidad Nacional de Frontera. Facultad de Ingeniería de Industrias Alimentarias - Peru
[6] Departamento de Ciências Básicas (ZAB). Faculdade de Zootecnia e Engenharia de Alimentos (FZEA/USP) - Brasil
Total Affiliations: 6
|
| Document type: | Journal article |
| Source: | Rev. Ciênc. Agron.; v. 53, 2021-11-24. |
| Abstract | |
ABSTRACT This study is based on the principle that vegetation indexes (VIs), derived from the RGB color model obtained from digital images, can be used to characterize spectral signatures and classify Brachiaria brizantha cv. Xaraés according to nitrogen status (N). From colorimetric data obtained from leaf blade images acquired in the field, three artificial neural networks were evaluated according to the performance in the classification of N status: Feedforward Backpropagation (FFBP), Cascade Forward Backpropagation (CFBP) and Radial Base function (RBFNN). Four N fertilization rates were applied to generate contrasting N contents in the plants. The youngest completely expanded leaves from 60 tillers were detached at each regrowth cycle of 28 days, thus their images and leaf N content were obtained. Samples were then classified as deficient (>17 g N kg-1 leaf dry matter (DM), moderately deficient (from 17.1 to 20.0 g N kg-1 DM), and sufficient (> 20.1 g N kg-1 DM). The VIs were selected by principal component analysis and the performance of the networks evaluated by the accuracy. The accuracy in classification obtained by the networks were 88%, 86% and 79% for FFBP, CFBP and RBFNN, respectively, indicating that the spectral signatures can be determined from images acquired in the field. So, the proposed method could be used to develop a software that aims to monitor the status of N in real time, providing a fast and inexpensive tool for defining the time and the amount of N fertilizer, according to the pasture demand. (AU) | |
| FAPESP's process: | 20/00345-1 - Determination of nitrogen status in Mavuno pastures through image analysis using artificial neural networks (ANN) |
| Grantee: | Adriano Rogério Bruno Tech |
| Support Opportunities: | Regular Research Grants |