Indirect Quantitative Analysis of Biochemical Parameters in Banana Using Spectral Reflectance Indices Combined with Machine Learning Modeling

Galal, Hoda; Elsayed, Salah; Allam, Aida; Farouk, Mohamed

doi:10.3390/horticulturae8050438

Cited by 7 publications

(1 citation statement)

References 45 publications

(67 reference statements)

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“…A trained NN can be deployed and used in the estimation of WW parameters in the time-scale of milliseconds to minutes [1], which is fitting in a process control of a WW treatment facility. In addition to the theoretical basis of the reliability of spectral reflectance for building estimation models [10], others have demonstrated the potential of artificial NN in using spectral reflectance data to model and estimate water quality parameters in large water bodies [11], properties of soil and rocks [12], and properties of crops [13,14]. The success of these works indicates the possible applicability of NN models in other, similar tasks such as estimating WW properties with potential applications in the process control of WW treatment facilities.…”

Section: Introductionmentioning

confidence: 99%

Quantitating Wastewater Characteristic Parameters Using Neural Network Regression Modeling on Spectral Reflectance

Fortela,

Travis,

Mikolajczyk

et al. 2023

Clean Technol.

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Wastewater (WW) analysis is a critical step in various operations, such as the control of a WW treatment facility, and speeding up the analysis of WW quality can significantly improve such operations. This work demonstrates the capability of neural network (NN) regression models to estimate WW characteristic properties such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonia (NH3-N), total dissolved substances (TDS), total alkalinity (TA), and total hardness (TH) by training on WW spectral reflectance in the visible to near-infrared spectrum (400–2000 nm). The dataset contains samples of spectral reflectance intensity, which were the inputs, and the WW parameter levels (BOD, COD, NH3-N, TDS, TA, and TH), which were the outputs. Various NN model configurations were evaluated in terms of regression model fitness. The mean-absolute-error (MAE) was used as the metric for training and testing the NN models, and the coefficient of determination (R2) between the model predictions and true values was also computed to measure how well the NN models predict the true values. The highest R2 (0.994 for training set and 0.973 for testing set) and lowest MAE (0.573 mg/L BOD, 6.258 mg/L COD, 0.369 mg/L NH3-N, 6.98 mg/L TDS, 2.586 m/L TA, and 0.014 mmol/L TH) were achieved when NN models were configured for single-variable output compared to multiple-variables output. Hyperparameter grid-search and k-fold cross-validation improved the NN model prediction performance. With online spectral measurements, the trained neural network model can provide non-contact and real-time estimation of WW quality at minimum estimation error.

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Section: Introductionmentioning

confidence: 99%

Quantitating Wastewater Characteristic Parameters Using Neural Network Regression Modeling on Spectral Reflectance

Fortela,

Travis,

Mikolajczyk

et al. 2023

Clean Technol.

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Deep learning based intelligent identification system for ripening stages of banana

Shuprajhaa

Raj

Kumar

et al. 2023

Postharvest Biology and Technology

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Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies

Elmetwalli,

Derbala,

Alsudays

et al. 2024

PLoS ONE

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Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non–destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits.

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Indirect Quantitative Analysis of Biochemical Parameters in Banana Using Spectral Reflectance Indices Combined with Machine Learning Modeling

Cited by 7 publications

References 45 publications

Quantitating Wastewater Characteristic Parameters Using Neural Network Regression Modeling on Spectral Reflectance

Quantitating Wastewater Characteristic Parameters Using Neural Network Regression Modeling on Spectral Reflectance

Deep learning based intelligent identification system for ripening stages of banana

Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies

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