Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression

Crusiol, Luís Guilherme Teixeira; Nanni, Marcos Rafael; Furlanetto, Renato Herrig; Sibaldelli, R. N. R.; Cézar, Everson; Sun, Ling; Foloni, José Salvador Simoneti; Mertz-Henning, L. M.; Nepomuceno, Alexandre Lima; Neumaier, N.; Farias, J. R. B.

doi:10.3390/rs13050977

Cited by 14 publications

(4 citation statements)

References 71 publications

(95 reference statements)

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“…PLSR is a nonparametric regression analysis method based on factor analysis, which is highly suitable for modeling under the condition of a small number of high-dimensional samples. Many studies have shown that it has excellent performance in the spectral estimation of soil material content [44][45][46]. Table 3 indicates that the optimal characteristic bands of soil petroleum hydrocarbon hyperspectral imaging selected using GARF can extract most of the important information in only a small amount of bands: CR-GARF had the best effect, in which 82% of the important information was contained in 17 characteristic bands (420, 1220, 1230, 1720, 1760, 1780, 1790, 1830, 2190, 2210, 2260, 2300, 2310, 2340, 2350, 2360, 2390 nm, only 8.37% of entire bands).…”

Section: Estimation Accuracies Of Soil Petroleum Hydrocarbon Contentmentioning

confidence: 99%

Hyperspectral Characteristic Band Selection and Estimation Content of Soil Petroleum Hydrocarbon Based on GARF-PLSR

Shi

Jiang

2023

J. Imaging

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With continuous improvements in oil production, the environmental problems caused by oil exploitation are becoming increasingly serious. Rapid and accurate estimation of soil petroleum hydrocarbon content is of great significance to the investigation and restoration of environments in oil-producing areas. In this study, the content of petroleum hydrocarbon and the hyperspectral data of soil samples collected from an oil-producing area were measured. For the hyperspectral data, spectral transforms, including continuum removal (CR), first- and second-order differential (CR-FD, CR-SD), and Napierian logarithm (CR-LN), were applied to eliminate background noise. At present, there are some shortcomings in the method of feature band selection, such as large quantity, time of calculation, and unclear importance of each feature band obtained. Meanwhile, redundant bands easily exist in the feature set, which seriously affects the accuracy of the inversion algorithm. In order to solve the above problems, a new method (GARF) for hyperspectral characteristic band selection was proposed. It combined the advantage that the grouping search algorithm can effectively reduce the calculation time with the advantage that the point-by-point search algorithm can determine the importance of each band, which provided a clearer direction for further spectroscopic research. The 17 selected bands were used as the input data of partial least squares regression (PLSR) and K-nearest neighbor (KNN) algorithms to estimate soil petroleum hydrocarbon content, and the leave-one-out method was used for cross-validation. The root mean squared error (RMSE) and coefficient of determination (R2) of the estimation result were 3.52 and 0.90, which implemented a high accuracy with only 8.37% of the entire bands. The results showed that compared with the traditional characteristic band selection methods, GARF can effectively reduce the redundant bands and screen out the optimal characteristic bands in the hyperspectral data of soil petroleum hydrocarbon with the method of importance assessment, which retained the physical meaning. It provided a new idea for the research of other substances in soil.

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Section: Estimation Accuracies Of Soil Petroleum Hydrocarbon Contentmentioning

confidence: 99%

Hyperspectral Characteristic Band Selection and Estimation Content of Soil Petroleum Hydrocarbon Based on GARF-PLSR

Shi

Jiang

2023

J. Imaging

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“…As a result, the spectral data were transformed into a linear model composed of waveband scaling coefficients [41]. PLSR (p ≤ 0.05) was performed by the Unscrambler ® (CAMO Software, Oslo, Norway) based on the optimal number of latent variable, as indicated by the lowest value of root mean square error (RMSE) through the leave-oneout cross-validation method, the highest coefficient of determination (R 2 ) of multivariate regression, and the value of systematic error (BIAS) close to zero [42]. The accuracy of the PLSR models was assessed with the Pearson coefficient (r) and the coefficient of determination (R 2 ) from the linear regression between observed and predicted LWC during the leave-one-out cross-validation procedure.…”

Section: Partial Least Squares Regression (Plsr) For Lwc Monitoringmentioning

confidence: 99%

In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data

Crusiol

Sun

et al. 2022

Sustainability

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China is one the largest maize (Zea mays L.) producer worldwide. Considering water deficit as one of the most important limiting factors for crop yield stability, remote sensing technology has been successfully used to monitor water relations in the soil–plant–atmosphere system through canopy and leaf reflectance, contributing to the better management of water under precision agriculture practices and the quantification of dynamic traits. This research was aimed to evaluate the relation between maize leaf water content (LWC) and ground-based and unoccupied aerial vehicle (UAV)-based hyperspectral data using the following approaches: (I) single wavelengths, (II) broadband reflectance and vegetation indices, (III) optimum hyperspectral vegetation indices (HVIs), and (IV) partial least squares regression (PLSR). A field experiment was undertaken at the Chinese Academy of Agricultural Sciences, Beijing, China, during the 2020 cropping season following a split plot model in a randomized complete block design with three blocks. Three maize varieties were subjected to three differential irrigation schedules. Leaf-based reflectance (400–2500 nm) was measured with a FieldSpec 4 spectroradiometer, and canopy-based reflectance (400–1000 nm) was collected with a Pika-L hyperspectral camera mounted on a UAV at three assessment days. Both sensors demonstrated similar shapes in the spectral response from the leaves and canopy, with differences in reflectance intensity across near-infrared wavelengths. Ground-based hyperspectral data outperformed UAV-based data for LWC monitoring, especially when using the full spectra (Vis–NIR–SWIR). The HVI and the PLSR models were demonstrated to be more suitable for LWC monitoring, with a higher HVI accuracy. The optimal band combinations for HVI were centered between 628 and 824 nm (R2 from 0.28 to 0.49) using the UAV-based sensor and were consistently located around 1431–1464 nm and 2115–2331 nm (R2 from 0.59 to 0.80) using the ground-based sensor on the three assessment days. The obtained results indicate the potential for the complementary use of ground-based and UAV-based hyperspectral data for maize LWC monitoring.

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“…In the 2017-2018, 2018-2019, and 2019-2020 crop seasons, Brazil consolidated its status as the world's largest producer, with productions of 123.4, 119.7, and 126.0 million tons (World Agricultural Production-USDA 2022). However, global and national production is constantly threatened by various biotic and abiotic factors [1][2][3][4][5][6][7][8], which can compromise grain yield and producer profitability.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Data for Early Identification and Classification of Potassium Deficiency in Soybean Plants (Glycine max (L.) Merrill)

Furlanetto,

Crusiol,

Nanni

et al. 2024

Remote Sensing

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Identifying potassium (K+) deficiency in plants has traditionally been a difficult and expensive process. Traditional methods involve inspecting leaves for symptoms and conducting a laboratory analysis. These methods are not only time-consuming but also use toxic reagents. Additionally, the analysis is performed during the reproductive stage of growth, which does not allow enough time for corrective fertilization. Moreover, soybean growers do not have other tools to analyze the nutrition status during the earlier stages of development. Thus, this study proposes a quick approach for monitoring K+ in soybean crops using hyperspectral data through principal component analysis (PCA) and linear discriminant analysis (LDA) with a wavelength selection algorithm. The experiment was carried out at the Brazilian National Soybean Research Center in the 2017–2018, 2018–2019, and 2019–2020 soybean crop seasons, at the stages of development V4–V5, R1–R2, R3–R4, and R5.1–R5.3. Three treatments were evaluated that varied in K+ availability: severe potassium deficiency (SPD), moderate potassium deficiency (MPD), and an adequate supply of potassium (ASP). Spectral data were collected using an ASD Fieldspec 3 Jr. hyperspectral sensor. The results showed a variation in the leaf spectral signature based on the K+ availability, with SPD having higher reflectance in the visible region due to a lower concentration of pigments. PCA explained 100% of the variance across all stages and seasons, making it possible to distinguish SPD at an early development stage. LDA showed over 70% and 59% classification accuracies for discriminating a K+ deficiency in the simulation and validation stages. This study demonstrates the potential of the method as a rapid nondestructive and accurate tool for identifying K+ deficiency in soybean leaves.

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Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression

Cited by 14 publications

References 71 publications

Hyperspectral Characteristic Band Selection and Estimation Content of Soil Petroleum Hydrocarbon Based on GARF-PLSR

Hyperspectral Characteristic Band Selection and Estimation Content of Soil Petroleum Hydrocarbon Based on GARF-PLSR

In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data

Hyperspectral Data for Early Identification and Classification of Potassium Deficiency in Soybean Plants (Glycine max (L.) Merrill)

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