Estimation of chlorophyll content in radish leaves using hyperspectral remote sensing data and machine learning algorithms

Nofrizal, Adenan Yandra; Sonobe, Rei; Yamashita, Hiroto; Ikka, Takashi; Morita, Akio

doi:10.1117/12.2600072

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Another technique used is standard normal variation (SNV) [25], which highlights important patterns and relationships between different bands. On the other hand, the detrending technique (DT) corrects the trend of the data [26].…”

Section: Spectroscopy An Alternative To Assess Soil Fertilitymentioning

confidence: 99%

Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification by Hyperspectral Imaging

Rodríguez Monroy,

Ortega Monsalve,

Fernando Galeano-Vasco

et al. 2024

Agricultural Sciences

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This chapter provides an overview of cation exchange capacity (CEC) and its importance as an indicator of soil fertility, particularly in the assessment of grassland quality. The limitations of traditional methods are highlighted, and the need to explore more agile approaches to grassland quality assessment is emphasized. The increasing use of hyperspectral information (HSI) as an accurate tool for measuring soil properties, which promotes more effective and sustainable rangeland management, is further explored. This provides data on soil fertility and forage quality, enabling more accurate decisions. The benefits and challenges of using HSI data to estimate CEC and its potential to improve pasture and forage production will also be examined. HSI technology allows information to be collected and analyzed from reflected light at different wavelengths, providing a clear understanding of soil physical and chemical properties. In addition, a case study illustrating the estimation of CIC using hyperspectral cameras in the department of Antioquia, Colombia, is presented. The chapter emphasizes the relevance of this topic in the rangeland context and concludes with a future outlook that anticipates a change in the management and understanding of grazing systems.

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Section: Spectroscopy An Alternative To Assess Soil Fertilitymentioning

confidence: 99%

Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification by Hyperspectral Imaging

Rodríguez Monroy,

Ortega Monsalve,

Fernando Galeano-Vasco

et al. 2024

Agricultural Sciences

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“…However, the leaf dry weight and thickness often make the results of such meters ambiguous [4,5], and the use of these devices is restricted. Alternative techniques based on hyperspectral remote reflectance using portable spectroradiometers, such as the Ocean Optics hyperspectral visible and near-infrared (Vis-NIR) spectroradiometer [6,7] and the Analytical Spectral Devices (ASD) FieldSpec series [8][9][10], have been proposed. Reflectance in the blue (420-470 nm) and red (640-680 nm) wavelength ranges depends on the leaf pigment, especially that due to chlorophyll, and a peak in the green region (520-580 nm) indicates a high chlorophyll content [11].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a One-Dimensional Convolution Neural Network for Chlorophyll Content Estimation Using a Compact Spectrometer

et al. 2022

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Leaf chlorophyll content is used as a major indicator of plant stress and growth, and hyperspectral remote sensing is frequently used to monitor the chlorophyll content. Hyperspectral reflectance has been used to evaluate vegetation properties such as pigment content, plant structure and physiological features using portable spectroradiometers. However, the prices of these devices have not yet decreased to consumer-affordable levels, which prevents widespread use. In this study, a system based on a cost-effective fingertip-sized spectrometer (Colorcompass-LF, a total price for the proposed solution was approximately 1600 USD) was evaluated for its ability to estimate the chlorophyll contents of radish and wasabi leaves and was compared with the Analytical Spectral Devices FieldSpec4. The chlorophyll contents per leaf area (cm2) of radish were generally higher than those of wasabi and ranged from 42.20 to 94.39 μg/cm2 and 11.39 to 40.40 μg/cm2 for radish and wasabi, respectively. The chlorophyll content was estimated using regression models based on a one-dimensional convolutional neural network (1D-CNN) that was generated after the original reflectance from the spectrometer measurements was de-noised. The results from an independent validation dataset confirmed the good performance of the Colorcompass-LF after spectral correction using a second-degree polynomial, and very similar estimation accuracies were obtained for the measurements from the FieldSpec4. The coefficients of determination of the regression models based on 1D-CNN were almost same (with R2 = 0.94) and the ratios of performance to deviation based on reflectance after spectral correction using a second-degree polynomial for the Colorcompass-LF and the FieldSpec4 were 4.31 and 4.33, respectively.

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Migrating from Invasive to Noninvasive Techniques for Enhanced Leaf Chlorophyll Content Estimations Efficiency

Kandpal

Kumar

2023

Critical Reviews in Analytical Chemistry

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Estimation of chlorophyll content in radish leaves using hyperspectral remote sensing data and machine learning algorithms

Cited by 3 publications

References 25 publications

Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification by Hyperspectral Imaging

Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification by Hyperspectral Imaging

Evaluation of a One-Dimensional Convolution Neural Network for Chlorophyll Content Estimation Using a Compact Spectrometer

Migrating from Invasive to Noninvasive Techniques for Enhanced Leaf Chlorophyll Content Estimations Efficiency

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