Determination of Significant Wavelengths and Prediction of Nitrogen Content for Citrus

Min, Manki; Lee, W. S.

doi:10.13031/2013.18308

Cited by 112 publications

(17 citation statements)

References 13 publications

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“…With the high availability of UAVs, the evaluation of nutritional condition in plants with high-spatial-resolution images has turned into common practice. Often used statistical methods like the principal component analysis (PCA), partial least square regression (PLSE), stepwise multiple linear regression (SMLR), and others were already implemented in the nitrogen content analysis [30,31]. However, these methods presented different predictions accuracies in this task.…”

Section: Related Workmentioning

confidence: 99%

“…In citrus plants, until this moment, no machine learning model was implemented to predict CNC. Still, one approximation was conducted with hyperspectral measurements in orange-leaves [31]. The authors applied a PLSR in a 350 to 2500 nm spectrum and discovered that the 448, 669, 719, 1377, 1773, and 2231 nm wavelengths were better correlated with LNC, and returned an R 2 of 0.83 and an RMSE of 0.122% for the validation dataset.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

et al. 2019

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“…where  is the X-weight,  the X-loading, and  the Y-weight. The B coefficients indicate the importance of each independent variable for PLS regression analysis where the higher absolute value is, the more it influences model performance (Min and Lee, 2005). The | | > 0.5 criterion was applied to select wavelengths that affect the moisture content of grafted cucumber seedlings in this study.…”

Section: Statistical Model Development and Significant Wavelength Anamentioning

confidence: 99%

Shortwave Infrared Hyperspectral Imaging Can Predict Moisture Content in Grafted Cucumber Seedlings

Jang,

Hwang,

Lee

et al. 2018

HST

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This study aimed to develop statistical model for predicting the moisture content in grafted cucumber (Cucumis sativus L.) by analyzing shortwave infrared hyperspectral images of scions of cucumber seedlings in the range of 1,000-2,400 nm. These images were processed using brightness correction and two sets of data were created by normalizing the spectral reflectance and conducting the first derivative. Statistical analysis of the spectral reflectance and moisture content of the grafted cucumber scions was used to interpret the correlation coefficient spectrum, multiple linear regression, and partial least square (PLS) regression to select the significant wavelengths and develop a model that could predict moisture content. The normalized PLS regression model showed better prediction performance than the other analysis methods tested. The determination coefficient and the root mean square difference for the validation dataset were 0.66 and 1.25%, respectively. These results suggest that the moisture content of grafted cucumber seedlings can be predicted in a nondestructive manner using hyperspectral images with a proper model.

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“…To minimize environmental concerns, farmers need to find a way to optimize nitrogen fertilizer application efficiency. Traditional fertilizer management methods were largely based on either soil or plant analysis, which are expensive, slow or labor-intensive (Min & Lee, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing of canopy reflectance can be used to sample a plant population or community rather than individual plants and to rapidly assess the spatial variability of a crop field (Feng et al, 2008). Remote sensing also has the potential to address environmental issues and to save cost, time, and labor (Min & Lee, 2005). Therefore, the application of remote sensing systems, which can be used to study large areas, has increased in agricultural management, mainly due to the improvement in the spatial, spectral, and temporal resolutions of remotely sensed observations (Karimi et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Multispectral remote sensing for site-specific nitrogen fertilizer management

Bagheri

Ahmadi

Alavipanah

et al. 2013

Pesq. agropec. bras.

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-The objective of this work was to evaluate the use of multispectral remote sensing for site-specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (Aster) was acquired in a 23 ha corn-planted area in Iran. For the collection of field samples, a total of 53 pixels were selected by systematic randomized sampling. The total nitrogen content in corn leaf tissues in these pixels was evaluated. To predict corn canopy nitrogen content, different vegetation indices, such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (Savi), optimized soil-adjusted vegetation index (Osavi), modified chlorophyll absorption ratio index 2 (MCARI2), and modified triangle vegetation index 2 (MTVI2), were investigated. The supervised classification technique using the spectral angle mapper classifier (SAM) was performed to generate a nitrogen fertilization map. The MTVI2 presented the highest correlation (R 2 =0.87) and is a good predictor of corn canopy nitrogen content in the V13 stage, at 60 days after cultivating. Aster imagery can be used to predict nitrogen status in corn canopy. Classification results indicate three levels of required nitrogen per pixel: low (0-2.5 kg), medium (2.5-3 kg), and high (3-3.3 kg).

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Determination of Significant Wavelengths and Prediction of Nitrogen Content for Citrus

Cited by 112 publications

References 13 publications

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

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

Shortwave Infrared Hyperspectral Imaging Can Predict Moisture Content in Grafted Cucumber Seedlings

Multispectral remote sensing for site-specific nitrogen fertilizer management

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