Yield prediction and validation of onion (Allium cepa L.) using key variables in narrowband hyperspectral imagery and effective accumulated temperature

Kang, Ye-Seong; Jang, Si Hyeong; Park, Jun Woo; Song, Hye Young; Ryu, Chung Ho; Jun, Sae Rom; Kim, Seong Heon

doi:10.1016/j.compag.2020.105667

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…The models were developed by sequentially applying latent variables from 1 to 15 in PLS and an alpha of 0.001, 0.01, 0.1, and 1 in RR and GP, and then the parameters of the model with the lowest MSE were selected. The possibility of reducing computational costs and improving reproducibility was considered by comparing the performances of models using the full bands and selected key bands [29]. The key bands were selected by comparing the R 2 of the calibration (prediction) and validation models depending on the combination of bands listed through the rank of importance of each band using the variance importance in the projection method for the PLS-based model and the Shapley additive explanation (SHAP) method for the RR-and GP-based models.…”

Section: Discussionmentioning

confidence: 99%

“…The variance importance in the projection value is calculated as the importance of each predictor, reflecting the weighted sum of squares of the PLSR weights [29]. The VIP can synthetize the contributions of the predictor and response variables, and the variables with a VIP value greater than 0.8 or 1 are considered significant [34].…”

Section: Variance Importance In Projectionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of the Total Nonstructural Carbohydrate Concentration in Apple Trees Using Hyperspectral Imaging

Kang,

Park,

Kim

et al. 2023

Horticulturae

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The total nonstructural carbohydrate (TNC) concentration is an important indicator of the growth period and health of fruit trees. Remote sensing can be applied to monitor the TNC concentration in crops in a non-destructive manner. In this study, hyperspectral imaging from an unmanned aerial vehicle was applied to estimate the TNC concentration in apple trees. Partial least-squares regression, ridge regression, and Gaussian process regression (GP) were used to develop estimation models, and their effectiveness using selected key bands as opposed to full bands was evaluated in an effort to reduce computational costs and improve reproducibility. Nine key bands were identified, and the GP-based model using these key bands performed almost as well as the models using full bands. These results can be combined with previous studies on estimating the nitrogen concentration to provide useful information for more precise nutrient management to improve the yield and quality of apple trees.

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Section: Discussionmentioning

confidence: 99%

Section: Variance Importance In Projectionmentioning

confidence: 99%

Estimation of the Total Nonstructural Carbohydrate Concentration in Apple Trees Using Hyperspectral Imaging

Kang,

Park,

Kim

et al. 2023

Horticulturae

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“…Variable importance in projection (VIP) is one of the most frequently used methods for variable selection in chemometrics, and the VIP scores selection method has been extensively used in various agricultural fields [24,30]. It summarizes the influence of individual x variables on the partial least squares (PLS) model and provides a measure that is useful for selecting x variables that contribute most to the y variance explanation [31].…”

Section: Variable Importance In Projectionmentioning

confidence: 99%

Determination of Internal Quality Indices in Oriental Melon Using Snapshot-Type Hyperspectral Image and Machine Learning Model

et al. 2022

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In this study, we aimed to develop a prediction model of the solid solutions concentration (SSC) and moisture content (MC) in oriental melon with snapshot-type hyperspectral imagery (Visible (VIS): 460–600 nm, 16 bands; Red-Near infrared (Red-NIR): 600–860 nm, 15 bands) using a machine learning model. The oriental melons were cultivated in a hydroponic greenhouse, Republic of Korea, and a total of 91 oriental melons that were harvested from March to April of 2022 were used as samples. The SSC and MC of the oriental melons were measured using destructive methods after taking hyperspectral imagery of the oriental melons. The reflectance spectrum obtained from the hyperspectral imagery was processed by the standard normal variate (SNV) method. Variable importance in projection (VIP) scores were used to select the bands related to SSC and MC. As a result, ten (609, 736, 561, 849, 818, 489, 754, 526, 683, and 597 nm) and six (609, 736, 561, 818, 849, and 489 nm) bands were selected for the SSC and MC, respectively. Four machine learning models, support vector regression (SVR), ridge regression (RR), K-nearest neighbors regression (K-NNR), and random forest regression (RFR), were used to develop models to predict SSC and MC, and their performances were compared. The SVR showed the best performance for predicting both the SSC and MC of the oriental melons. The SVR model achieved a relatively high accuracy with R2 values of 0.86 and 0.74 and RMSE values of 1.06 and 1.05 for SSC and MC, respectively. However, it will be necessary to carry out more experiments under various conditions, such as differing maturities of fruits and varying light sources and environments, to achieve more comprehensive predictions and apply them to monitoring robots in the future. Nevertheless, it is considered that the snapshot-type hyperspectral imagery aided by SVR would be a useful tool to predict the SSC and MC of oriental melon. In addition, if the maturity classification model for the oriental melon can be applied to fields, it could lead to less labor and result in high-quality oriental melon production.

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“…This implies that model calibrations are needed for each cultivation region and year [55]. Some studies have suggested that applying climate data such as temperature, precipitation, and solar radiation as well as important spectral variables such as PI 1 and PI 2 is important to increase predictability on crop yield in other years [28,56]. As a result, when the cumulative temperature data for each year were applied in the mutual prediction of the onion yield model, the RMSEP was lower, with a difference of about 16%.…”

Section: Comparison With and Extension Of Related Studiesmentioning

confidence: 99%

“…Among the various regression methods for the prediction of rice parameters, it is important to select a regression method that can be developed as a year-invariant model. The potential of developing a yearinvariant model must be verified through cross-validation for different years via the collection of multiyear data [28]. However, although the model is based on multiyear data, changes in the environmental conditions at various fields may make it difficult to reproduce the model [18].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery

et al. 2021

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Unmanned aerial vehicle-based multispectral imagery including five spectral bands (blue, green, red, red-edge, and near-infrared) for a rice field in the ripening stage was used to develop regression models for predicting the rice yield and protein content and to select the most suitable regression analysis method for the year-invariant model: partial least squares regression, ridge regression, and artificial neural network (ANN). The regression models developed with six vegetation indices (green normalization difference vegetation index (GNDVI), normalization difference red-edge index (NDRE), chlorophyll index red edge (CIrededge), difference NIR/Green green difference vegetation index (GDVI), green-red NDVI (GRNDVI), and medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI)), calculated from the spectral bands, were applied to single years (2018, 2019, and 2020) and multiple years (2018 + 2019, 2018 + 2020, 2019 + 2020, and all years). The regression models were cross-validated through mutual prediction against the vegetation indices in nonoverlapping years, and the prediction errors were evaluated via root mean squared error of prediction (RMSEP). The ANN model was reproducible, with low and sustained prediction errors of 24.2 kg/1000 m2 ≤ RMSEP ≤ 59.1 kg/1000 m2 in rice yield and 0.14% ≤ RMSEP ≤ 0.28% in rice-protein content in all single-year and multiple-year analyses. When the importance of each vegetation index of the regression models was evaluated, only the ANN model showed the same ranking in the vegetation index of the first (MTCI in both rice yield and protein content) and second importance (CIrededge in rice yield and GRNDVI in rice-protein content). Overall, this means that the ANN model has the highest potential for developing a year-invariant model with stable RMSEP and consistent variable ranking.

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Yield prediction and validation of onion (Allium cepa L.) using key variables in narrowband hyperspectral imagery and effective accumulated temperature

Cited by 12 publications

References 26 publications

Estimation of the Total Nonstructural Carbohydrate Concentration in Apple Trees Using Hyperspectral Imaging

Estimation of the Total Nonstructural Carbohydrate Concentration in Apple Trees Using Hyperspectral Imaging

Determination of Internal Quality Indices in Oriental Melon Using Snapshot-Type Hyperspectral Image and Machine Learning Model

Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery

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