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Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

2019; Multidisciplinary Digital Publishing Institute; Volume: 11; Issue: 24 Linguagem: Inglês

10.3390/rs11242925

2072-4292

Lucas Prado Osco, Ana Paula Marques Ramos, Danilo Roberto Pereira, Érika Akemi Saito Moriya, Nilton Nobuhiro Imai, Edson Takashi Matsubara, Nayara Vasconcelos Estrabis, Maurício de Souza, José Marcato, Wesley Nunes Gonçalves, Jonathan Li, Veraldo Liesenberg, José Eduardo Creste,

Leaf Properties and Growth Measurement

The traditional method of measuring nitrogen content in plants is a time-consuming and labor-intensive task. Spectral vegetation indices extracted from unmanned aerial vehicle (UAV) images and machine learning algorithms have been proved effective in assisting nutritional analysis in plants. Still, this analysis has not considered the combination of spectral indices and machine learning algorithms to predict nitrogen in tree-canopy structures. This paper proposes a new framework to infer the nitrogen content in citrus-tree at a canopy-level using spectral vegetation indices processed with the random forest algorithm. A total of 33 spectral indices were estimated from multispectral images acquired with a UAV-based sensor. Leaf samples were gathered from different planting-fields and the leaf nitrogen content (LNC) was measured in the laboratory, and later converted into the canopy nitrogen content (CNC). To evaluate the robustness of the proposed framework, we compared it with other machine learning algorithms. We used 33,600 citrus trees to evaluate the performance of the machine learning models. The random forest algorithm had higher performance in predicting CNC than all models tested, reaching an R2 of 0.90, MAE of 0.341 g·kg−1 and MSE of 0.307 g·kg−1. We demonstrated that our approach is able to reduce the need for chemical analysis of the leaf tissue and optimizes citrus orchard CNC monitoring.