Hyperspectral remote sensing for foliar nutrient detection in forestry: A near-infrared perspective

Singh, Lorraine; Mutanga, Onisimo; Mafongoya, Paramu; Peerbhay, Kabir; Crous, Jacob

doi:10.1016/j.rsase.2021.100676

Cited by 6 publications

(3 citation statements)

References 71 publications

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“…Los productos de origen orgánico interaccionan de manera distinta a la radiación electromagnética, debido a su naturaleza (Singh et al 2022), por lo que no es posible comparar los resultados obtenidos en este estudio con otras especies; sin embargo, Cozzolino & Moron (2004) demostraron la eficiencia del uso de la espectroscopía visible y NIRS, para la predicción de nutrientes en especies de leguminosas.…”

Section: Resultados Y Discusiónunclassified

Espectroscopía visible y del infrarrojo cercano para el análisis de nutrientes en tejido vegetal de caña de azúcar para producción de panela

Camargo-Hernández

Parra-Forero

Varón-Ramírez

et al. 2023

Rev. U.D.C.A Act. & Div. Cient.

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La espectroscopía de reflectancia en el infrarrojo cercano (NIRS) es una tecnología rápida, multiparamétrica, amigable con el ambiente, de bajo costo y gran exactitud, para el análisis de diversos componentes en alimentos, en suelo y en agricultura. El objetivo del presente estudio fue construir modelos de calibración NIRS, para la predicción de nutrientes en tejido vegetal de caña de azúcar, para producción de panela, cultivada en la región de la Hoya del río Suárez. Un total de 416 muestras de tejido fueron escaneadas en el segmento espectral Vis-NIR. El análisis quimiométrico, se realizó con el software WinISI V4.10, aplicando la regresión de mínimos cuadrados parciales modificados, junto a una validación cruzada. Se evaluaron cuatro modelos con diferentes tratamientos matemáticos y el rendimiento de las calibraciones, se hizo por medio de la validación externa, analizando las medidas de bondad de ajuste, como el coeficiente de determinación de la predicción, el error estándar de la predicción ajustado por el sesgo y la desviación predictiva residual. Los resultados muestran que el modelo de calibración para N presentó el mayor poder predictivo. Para macronutrientes, las calibraciones, con mayor poder predictivo, fueron P y K y para micronutrientes, el modelo para B, mientras que para Cu presentó el más bajo poder predictivo. Se encontraron modelos adecuados para la predicción de los contenidos de N, Ca y P; para los demás nutrientes, se recomienda ampliar el conjunto de calibración.

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Section: Resultados Y Discusiónunclassified

Espectroscopía visible y del infrarrojo cercano para el análisis de nutrientes en tejido vegetal de caña de azúcar para producción de panela

Camargo-Hernández

Parra-Forero

Varón-Ramírez

et al. 2023

Rev. U.D.C.A Act. & Div. Cient.

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“…In recent years, several studies have reported various ecophysiological information about plants that can be evaluated using spectral characteristics (reflectance and transmittance) of leaves and canopies (Singh et al 2022). However, in this field of study, most work has been conducted using not transmitted but reflected light spectra because reflected light can be measured from the sky using satellites or drones for large-scale canopies.…”

Section: Discussionmentioning

confidence: 99%

Nondestructive Measurement Method of Leaf Area Index Using Near-infrared Radiation and Photosynthetically Active Radiation Transmitted through a Leafy Vegetable Canopy

Yamaguchi

Yasutake

Hirota

et al. 2023

horts

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Because the leaf area index (LAI) is an essential parameter for understanding the structure and growth status of plant canopies, nondestructive and continuous estimation methods have been required. Recently, an LAI estimation method using the ratio of near-infrared radiation (NIR; 700–1000 nm) to photosynthetically active radiation (PAR; 400–700 nm) (NIRin/PARin) transmitted through a canopy has been proposed. However, because previous studies on this NIRin/PARin-based LAI estimation method are limited to tall plants (e.g., forest and rice canopies), in this study, we applied this method to a short canopy (i.e., spinach) and investigated its validity. NIRin/PARin and three other traditional indices for indirect LAI estimation—relative PPF density (rPPFD), normalized difference vegetation index (NDVI), and simple ratio (SR)—were measured in 25 canopies with different LAI. NIRin/PARin showed better estimation sensitivity (R2 = 0.88) to the observed LAI than the other three indices, particularly when LAI was greater than 3 m2·m−2. In addition, the LAI estimated from NIRin/PARin measured at 10-min intervals in the entire growth period could capture an increasing trend in the measured LAI throughout the entire growth stage (mean absolute error = 0.87 m2·m−2). Errors in long-term LAI estimations may be caused by the sensor location and insufficient data due to unsuitable weather conditions for measuring NIRin/PARin. The current study demonstrates the merits and limitations of the NIRin/PARin-based LAI estimation method applied to low height canopies, thereby contributing to its practical use in horticultural crops.

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“…8 The hyperspectral data of leaves are as regarded as human fingerprints, which can be used to reveal the nutritional status of the plant and the difference between the species of plants. 9,10 Yang et al utilized hyperspectral imaging technology combined with a recognition model based on particle swarm optimization-extreme learning machine to identify eight tree species at the leaf level. 11 Hideaki et al developed an approach based on near infrared hyperspectral imaging (NIR-HSI) technology and deep convolutional neural network to identify 38 hardwood species with an accuracy of 90.5%.…”

Section: Introductionmentioning

confidence: 99%

Leaf-based species classification of hybrid cherry tomato plants by using hyperspectral imaging

Wu²,

Zhao

et al. 2023

Journal of Near Infrared Spectroscopy

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Approaches based on near infrared hyperspectral imaging (NIR-HSI) technology combined with machine learning have been developed to classify the leaves of hybrid cherry tomatoes and then identify the species of hybrid cherry tomato plants. The near infrared (NIR) hyperspectral images of 400 cherry tomato leaves (100 per species) were collected in the wavelength range of 900–1700 nm. Machine learning algorithms such as linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM) were employed to construct leaf classification models with the hyperspectral data preprocessed by Savitzky-Golay (SG) smoothing filter, first derivative (first Der) and standard normal variate (SNV). Principle of Component Analysis (PCA) was also used to reduce the data dimension and extract spectral features. It is revealed that the LDA model reaches the highest classification accuracy among the three machine learning algorithms and SNV can lead to higher improvement in model accuracy than other preprocessing methods of SG smoothing and first Der. Analysis based on PCA spectral feature extraction demonstrates that differences occur in internal material content in the leaves of cherry tomato plants with different species, which renders the models being able to distinguish between the species. Another important work was performed to reveal the different effects of the mesophyll and vein regions (VR) on the accuracy of the leaf classification model. It is demonstrated that the classification accuracy is improved by a value of 0.033 or 0.042 when mesophyll substitutes vein or whole leaf as regions of interest (ROI) to extract reflectance spectra for modeling. As a result, the accuracy of the training and test set respectively reached a high value of 0.998 and 0.973 for the LDA classification model combined with the SNV preprocessing method. The results propose that the use of mesophyll region (MR) as ROI can improve the performance of the leaf classification model, which provides a new strategy for efficient and non-destructive classification of different hybrid cherry tomato plants.

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Hyperspectral remote sensing for foliar nutrient detection in forestry: A near-infrared perspective

Cited by 6 publications

References 71 publications

Espectroscopía visible y del infrarrojo cercano para el análisis de nutrientes en tejido vegetal de caña de azúcar para producción de panela

Espectroscopía visible y del infrarrojo cercano para el análisis de nutrientes en tejido vegetal de caña de azúcar para producción de panela

Nondestructive Measurement Method of Leaf Area Index Using Near-infrared Radiation and Photosynthetically Active Radiation Transmitted through a Leafy Vegetable Canopy

Leaf-based species classification of hybrid cherry tomato plants by using hyperspectral imaging

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