Use of VIS-NIR-SWIR spectroscopy for the prediction of water status in soybean plants in the Colombian Piedmont Plains

Rodríguez, Betty Jazmín Gutiérrez; Tovar, José Orlando Argüello; García-Navarrete, Oscar Leonardo

doi:10.15446/dyna.v86n210.78703

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Several frequently used spectral data preprocessing techniques were applied in this study, including light scatter and baseline correction (multiplicative scatter correction (MSC) and standard normal variate (SNV)) to correct light scattering variation and baseline in reflectance spectroscopy. In addition to decreasing scattering in the NIR [31], data enhancement (normalization and mean center) was also used to reduce redundant information. The median filter method reduces the effect of noise, thereby providing smoother spectra.…”

Section: Preprocessing Of Spectral Datamentioning

confidence: 99%

Using Machine Learning for Nutrient Content Detection of Aquaponics-Grown Plants Based on Spectral Data

et al. 2022

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Nutrients derived from fish feed are insufficient for optimal plant growth in aquaponics; therefore, they need to be supplemented. Thus, estimating the amount of supplementation needed can be achieved by looking at the nutrient contents of the plant. This study aims to develop trustworthy machine learning models to estimate the nitrogen (N), phosphorus (P), and potassium (K) contents of aquaponically grown lettuce. A FieldSpec4, Pro FR portable spectroradiometer (ASD Inc., Analytical Spectral Devices Boulder, Boulder, CO, USA) was used to measure leaf reflectance spectra, and 128 lettuce seedlings given four NPK treatments were used for spectra acquisition and total NPK estimation. Principal component analysis (PCA), genetic algorithms (GA), and sequential forward selection (SFS) were applied to select the optimal wavebands. Partial least squares regression (PLSR), back-propagation neural network (BPNN), and random forest (RF) approaches were used to develop the predictive models of NPK contents using the selected optimal wavelengths. Good and significantly correlated predictive accuracy was obtained in comparison with the laboratory-measured freshly cut lettuce leaves with R2 ≥ 0.94. The proposed approach provides a pathway toward automatic nutrient estimation of aquaponically grown lettuce. Consequently, aquaponics will become more intelligent, and will be adopted as a precision agriculture technology.

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Section: Preprocessing Of Spectral Datamentioning

confidence: 99%

Using Machine Learning for Nutrient Content Detection of Aquaponics-Grown Plants Based on Spectral Data

et al. 2022

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“…The spectral data obtained using ASD are in the form of an unprocessable file (.asd), and ViewSpecPro 6.02 software was used to convert the data into a Microsoft Excel values file (.csv) format. The average spectral data (X-variable), as well as the corresponding SPAD-502 readings (Y-variable), were stored in an Excel file to perform different preprocessing treatments, such as standard normal variate (SNV), to reduce the data scattering in the near-infrared (NIR) zone [41], and a Savitzky-Golay filter was used to smooth the spectral data [42]. Then, the data were divided into training (70%) and testing (30%) sets using a random subsampling technique to provide accurate analysis and prevent data bias.…”

Section: Spectral Dataset Collection and Preprocessingmentioning

confidence: 99%

High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images

Taha,

Mao,

Wang

et al. 2024

Plants

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Chlorophyll content reflects plants’ photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based model to estimate the chlorophyll content in aquaponically grown lettuce. A completely open-source automated machine learning (AutoML) framework (EvalML) was employed to develop the prediction models. The performance of AutoML along with four other standard machine learning models (back-propagation neural network (BPNN), partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM) was compared. The most sensitive spectral (SVIs) and color vegetation indices (CVIs) for chlorophyll content were extracted and evaluated as reliable estimators of chlorophyll content. Using an ASD FieldSpec 4 Hi-Res spectroradiometer and a portable red, green, and blue (RGB) camera, 3600 hyperspectral reflectance measurements and 800 RGB images were acquired from lettuce grown across a gradient of nutrient levels. Ground measurements of leaf chlorophyll were acquired using an SPAD-502 m calibrated via laboratory chemical analyses. The results revealed a strong relationship between chlorophyll content and SPAD-502 readings, with an R2 of 0.95 and a correlation coefficient (r) of 0.975. The developed AutoML models outperformed all traditional models, yielding the highest values of the coefficient of determination in prediction (Rp2) for all vegetation indices (VIs). The combination of SVIs and CVIs achieved the best prediction accuracy with the highest Rp2 values ranging from 0.89 to 0.98, respectively. This study demonstrated the feasibility of spectral and color vegetation indices as estimators of chlorophyll content. Furthermore, the developed AutoML models can be integrated into embedded devices to control nutrient cycles in aquaponics systems.

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“…Among them, visible (VIS, 400 to 700 nm), near infrared (NIR, 700 to 1,100 nm), and shortwave infrared (SWIR, 1,100 to 2,500 nm) reflectance spectroscopy (VIS-NIR-SWIR) measures the reflectance and/or transmittance of light by plants over a range of wavelengths. VIS-NIR-SWIR spectroscopy has been used to monitor the health status and marketability of crops [ 2 , 17 ]. Chlorophyll, anthocyanin, and water in leaves exhibit strong absorption peaks within the VIS-NIR-SWIR wavelengths, which provide essential information for plant health diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices

et al. 2023

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Leaf color patterns vary depending on leaf age, pathogen infection, and environmental and nutritional stresses; thus, they are widely used to diagnose plant health statuses in agricultural fields. The visible-near infrared-shortwave infrared (VIS-NIR-SWIR) sensor measures the leaf color pattern from a wide spectral range with high spectral resolution. However, spectral information has only been employed to understand general plant health statuses (e.g., vegetation index) or phytopigment contents, rather than pinpointing defects of specific metabolic or signaling pathways in plants. Here, we report feature engineering and machine learning methods that utilize VIS-NIR-SWIR leaf reflectance for robust plant health diagnostics, pinpointing physiological alterations associated with the stress hormone, abscisic acid (ABA). Leaf reflectance spectra of wild-type, ABA2 -overexpression, and deficient plants were collected under watered and drought conditions. Drought- and ABA-associated normalized reflectance indices (NRIs) were screened from all possible pairs of wavelength bands. Drought associated NRIs showed only a partial overlap with those related to ABA deficiency, but more NRIs were associated with drought due to additional spectral changes within the NIR wavelength range. Interpretable support vector machine classifiers built with 20 NRIs predicted treatment or genotype groups with an accuracy greater than those with conventional vegetation indices. Major selected NRIs were independent from leaf water content and chlorophyll content, 2 well-characterized physiological changes under drought. The screening of NRIs, streamlined with the development of simple classifiers, serves as the most efficient means of detecting reflectance bands that are highly relevant to characteristics of interest.

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Use of VIS-NIR-SWIR spectroscopy for the prediction of water status in soybean plants in the Colombian Piedmont Plains

Cited by 5 publications

References 15 publications

Using Machine Learning for Nutrient Content Detection of Aquaponics-Grown Plants Based on Spectral Data

Using Machine Learning for Nutrient Content Detection of Aquaponics-Grown Plants Based on Spectral Data

High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images

Classification of Plant Endogenous States Using Machine Learning-Derived Agricultural Indices

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