Evaluation of the nutritional status of corn by vegetation indices via aerial images

Andrade, A. S. de; Melo, F. de B.; Bastos, E. A.; Cardoso, M. J.

doi:10.1590/0103-8478cr20200692

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Furthermore, regarding the problems relevant to the topic in general, several research reports, articles, and papers discuss the relationship between the vegetation indices and nutritional values, [42,43]; the latter source examines data correlation between vegetation indices and the nitrogen nutrition index (NNI), the investigation being focused solely on pepper plants. The vegetation indices and the agronomic performance of corn varieties under different nitrogen rates are compared in article [44].…”

Section: Predicting Crop Yieldmentioning

confidence: 99%

Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging

et al. 2023

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Estimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This article examines and discusses the relationships between vegetation indices and nutritiolnal values that have been determined via chemical analysis of plant samples collected in the field. In this context, emphasis is placed on the normalized difference red edge index (NDRE), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and nutritional values, such as those of dry matter. The relationships between the variables were correlated and described by means of regression models. This produced equations that are applicable for estimating the quantity of dry matter and thus determining the optimum corn harvest time. The obtained equations were validated on five different types of corn hybrids in fields within the South Moravian Region, Moravia, the Czech Republic.

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Section: Predicting Crop Yieldmentioning

confidence: 99%

Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging

et al. 2023

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Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random Forests

Yu,

et al. 2023

Agronomy

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The information acquisition about potassium, which affects the quality and yield of crops, is of great significance for crop nutrient management and intelligent decision making in smart agriculture. This article proposes a method for predicting the rice leaf potassium content (LKC) using spectral characteristics and random forests (RF). The method screens spectral characteristic variables based on the linear correlation analysis results of rice LKC and four transformed spectra (original reflectance (R), first derivative reflectance (FDR), continuum-removed reflectance (CRR), and normalized reflectance (NR)) of leaves and the PCA dimensionality reduction results of vegetation indices. Following a second screening of the correlated single band and vegetation index variables of the four transformed spectra, the RF is used to obtain the mixed variable (MV), and regression models are developed to achieve an accurate prediction of rice LKC. Additionally, the effect of potassium spectral sensitivity bands, indices, spectral transformation form, and different modeling methods on rice LKC prediction accuracy is assessed. The results showed that the mixed variable obtained with the second screening using the random forest feature selection method could effectively improve the prediction accuracy of rice LKC. The regression models based on the single band variables (BV) and the vegetation index variables (IV), FDR–RF and IV–RF, with R2 values of 0.62301 and 0.7387 and RMSE values of 0.24174 and 0.15045, respectively, are the best models. In comparison to the previous two models, the MV–RF validation had a higher R2 and a lower RMSE, reaching 0.77817 and 0.14913, respectively. It can be seen that the RF has a better processing ability for the MV that contains vegetation indices and IV than for the BV. Furthermore, the results of different variable screening and regression analyses also revealed that the single band’s range of 1402–1428 nm and 1871–1907 nm, as well as the vegetation indices constituted of reflectance 1799–1881 nm and 2276–2350 nm, are of great significance for predicting rice LKC. This conclusion can provide a reference for establishing a universal vegetation index related to potassium.

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Adubação nitrogenada no cultivo do milho safrinha em duas regiões no Tocantins

Carvalho,

Santos,

Provenci

et al. 2023

Rev. Ciênc. Agrovet.

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O cultivo de milho safrinha representa a maior área plantada e produção da cultura no Brasil desde a safra 2011/2012, não sendo diferente no estado do Tocantins. O milho é um dos principais cereais produzidos no mundo e uma das espécies vegetais mais exigentes quanto ao Nitrogênio (N). A resposta positiva ao N está relacionada a diversos fatores bióticos e abióticos e que fundamenta a realização de estudos constantes, principalmente em novas fronteiras agrícolas. O objetivo do trabalho foi avaliar diferentes doses de adubação nitrogenada no cultivo de milho safrinha, em duas regiões no Tocantins. Para tanto, foram instalados dois experimentos de campo (Palmas - TO; Lagoa da Confusão - TO), na safrinha 2021/2022, sob o delineamento de blocos ao acaso com quatro repetições e seis tratamentos (doses de N em cobertura: 0, 40, 80, 120, 160 e 200 kg ha-1). Em Lagoa da Confusão - TO, a produtividade de grãos apresentou resposta linear com média de 5.786 kg ha-1 ao utilizar a dose máxima (200 kg ha-1 de N em cobertura), que foi 104% superior quando do não uso do N em cobertura. Em Palmas – TO, não houve efeito significativo do nitrogênio em cobertura e a produtividade média foi de 6.632 kg ha-1. As características relacionadas a qualidade da semente não apresentaram efeito do nitrogênio em cobertura e correlação com as características agronômicas. Estas, por sua vez, apresentaram forte associação com a produtividade de grãos.

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Evaluation of the nutritional status of corn by vegetation indices via aerial images

Cited by 3 publications

References 16 publications

Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging

Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging

Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random Forests

Adubação nitrogenada no cultivo do milho safrinha em duas regiões no Tocantins

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