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Identification and quantification of potassium (K+) deficiency in maize plants using an unmanned aerial vehicle and visible / near-infrared semi-professional digital camera

Furlanetto, Renato Herrig ; Rafael Nanni, Marco ; Guilherme Teixeira Crusiol, Luís ; Silva, Guilherme Fernando Capristo ; Junior, Adilson de Oliveira ; Sibaldelli, Rubson Natal Ribeiro

International journal of remote sensing, 2021-12, Vol.42 (23), p.8783-8804 [Periódico revisado por pares]

London: Taylor & Francis

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  • Título:
    Identification and quantification of potassium (K+) deficiency in maize plants using an unmanned aerial vehicle and visible / near-infrared semi-professional digital camera
  • Autor: Furlanetto, Renato Herrig ; Rafael Nanni, Marco ; Guilherme Teixeira Crusiol, Luís ; Silva, Guilherme Fernando Capristo ; Junior, Adilson de Oliveira ; Sibaldelli, Rubson Natal Ribeiro
  • Assuntos: Agricultural research ; Biological fertilization ; Calibration ; Cameras ; Cereal crops ; Corn ; Crop yield ; Developmental stages ; Digital cameras ; Evaluation ; Fertility ; Grain ; Identification ; Infrared cameras ; Leaves ; Mineral nutrients ; Monitoring ; Nondestructive testing ; Normalized difference vegetative index ; Nutrient content ; Nutrient deficiency ; Nutrients ; Photosynthesis ; Plant metabolism ; Potassium ; Research facilities ; Restoration ; Root-mean-square errors ; Soil fertility ; Soybeans ; Spectral bands ; Spectroradiometers ; Unmanned aerial vehicles ; Vegetation ; Vegetation index
  • É parte de: International journal of remote sensing, 2021-12, Vol.42 (23), p.8783-8804
  • Descrição: Brazil is one of the largest producers of maize worldwide. However, this production is threatened due to low soil fertility, especially low levels of potassium (K + ). K + is one of the most important nutrients in plant metabolism, acting on enzymatic activation and also on photosynthetic processes. The identification of its deficiency by using traditional methods is difficult with regard to timely restoration of the nutrient to adequate levels. Therefore, the use of low-cost modified cameras attached to Unmanned Aerial Vehicles (UAVs) are important tools for agricultural monitoring. Nevertheless, there are no reports of studies with the purpose of evaluating the monitoring of K + deficiency in maize crops using multispectral images captured from UAVs. Therefore, this study aimed at exploring the possibility of identifying K + deficiency and quantifying the nutrient leaf content by using a Vegetation Index (VI). The experiment was carried out at the National Soybean Research Centre (Embrapa Soja, a branch of the Brazilian Agricultural Research Corporation). The experimental plots were constantly managed in order to obtain different conditions of K + availability to plants, achieving levels that ranged from severe deficiency to an adequate nutrient level. The following treatments were established: severe potassium deficiency (SPD), moderate potassium deficiency (MPD) and adequate supply of potassium (ASP). The evaluations were performed in the Brazilian maize crop referred to as 'safrinha, at the V7, V12 and R3 developmental stages, with image capture covering the visible and near-infrared region, using two Fujifilm IS PRO digital cameras attached to an UAV. In these development stages, leaves were collected to determine tissue K + concentration. The images were radiometrically corrected with the support of calibration targets and reference values, using an Fieldspec 3 Jr. spectroradiometer. The VIs comprised the ratio among the red, green and infrared spectral bands, that is, green normalized difference vegetation index (GNDVI), normalized difference vegetation index (NDVI), ratio between infrared and green (GRVI), ratio between green and infrared (GNIR), ratio between red and infrared (RNIR) and ratio between infrared and red (RVI). Regarding all the treatments assessed, the results showed that foliar K + was statistically different. The VIs were efficient only in differentiating SPD and ASP treatments at all development stages evaluated. However, none were statistically significant for MPD. The linear regressions showed a high coefficient of determination (R 2 ) and low root mean square error (RMSE) value; the best prediction of K + concentration obtained was at V12 for regressions with these VIs: GRVI (R 2  = 0.79, RMSE 4.50 g kg −1 ) and RVI (R 2  = 0.71, RMSE 4.39 g kg −1 ). The grain yield values showed that SPD caused an average reduction of 5,645.90 kg ha −1 in relation to the ASP. Considering MPD, the grain yield was 1,242.00 kg ha −1 lower in comparison with ASP. In conclusion, estimating foliar K + content and identifying its deficiency in maize crops based on the VIs of multispectral images from cameras attached to UAVs is possible, which ensures agility to these evaluations in a non-destructive manner, improving efficiency of K + fertilization and providing farmers with a new tool.
  • Editor: London: Taylor & Francis
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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