Yield and Quality Prediction of Winter Rapeseed—Artificial Neural Network and Random Forest Models

Rajković, Dragana; Jeromela, Ana Marjanović; Pezo, Lato; Lončar, Biljana; Zanetti, Federica; Monti, Andrea; Špika, Ankica Kondić

doi:10.3390/agronomy12010058

Cited by 40 publications

(15 citation statements)

References 64 publications

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“…On the contrary, the model for the antioxidant activity seemed to predict data accurately (RMSE < 0.2) as well as those for the titratable acidity and the total soluble solids seemed to work well (Table 4). A recent study suggested that ANN modeling can be successfully exploited for the evaluation of the same qualitative parameters [61]. To estimate the relative importance of the input variables to ANN model predictions, sensitivity analysis was carried out.…”

Section: Prediction Of Strawberries Quality Attributesmentioning

confidence: 99%

Prediction of Strawberries’ Quality Parameters Using Artificial Neural Networks

Amoriello¹,

Ciccoritti

Ferrante

2022

Agronomy

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Strawberry is a very popular fruit, appreciated for its unique flavor and many beneficial traits such as antioxidants and useful amino acids, which strongly contribute to the overall quality of the product. Indeed, the quality of fresh fruit is a fundamental aspect for consumers, and it is crucial for the success of breeding activities as well as for enhancing the competitiveness and profitability of the fruit industry. Nowadays, the entire supply chain requires simple and fast systems for quality evaluation. In this context, the pomological and chemical traits (i.e., soluble solids, firmness, titratable acidity, dry matter) as well as nutritional ones such as total phenols, total anthocyanins and antioxidant potential were evaluated and compared for seven strawberry cultivars and three harvest times. The prediction of the qualitative traits was carried out using color space coordinates (L*, a* and b*) and two statistical techniques, i.e., the multiple linear regression models (MLR) and artificial neural networks (ANNs). Unsatisfactory prediction performances were obtained for all parameters when MLR was applied. On the contrary, the good prediction of the internal quality attributes, using ANN, was observed, especially for both antioxidant activity and the total monomeric anthocyanin (R2 = 0.906, and R2 = 0.943, respectively). This study highlighted that color coordinates coupled with ANN can be successfully used to evaluate the quality of strawberry.

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Section: Prediction Of Strawberries Quality Attributesmentioning

confidence: 99%

Prediction of Strawberries’ Quality Parameters Using Artificial Neural Networks

Amoriello¹,

Ciccoritti

Ferrante

2022

Agronomy

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“…In the studies by Amoriello et al [ 28 ] and Yoshioka et al [ 82 ], they discovered that ANN models could accurately predict anthocyanins by taking into account the CIELab coordinates of L*, a*, and b*. A recent study suggested that ANN modeling can be successfully exploited for the prediction of quality parameters of winter rapeseed [ 83 ]. Furthermore, ANN was developed to predict the TSS, titratable acidity, TSS/titratable acidity, anthocyanin, vitamin C, and total carotenoids contents using surface-color CIELab coordinates of L*, hue, and chroma for fresh peach fruit based on inputs of juice volume, single fruit weight, and sphericity percent [ 84 ].…”

Section: Resultsmentioning

confidence: 99%

Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

Al-Saif

Abdel-Sattar

Aboukarima

et al. 2022

Foods

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The grape is a very well-liked fruit that is valued for its distinct flavor and several health benefits, including antioxidants, anthocyanins, soluble sugars, minerals, phenolics, flavonoids, organic acids, and vitamins, which significantly improve the product’s overall quality. Today’s supply chain as a whole needs quick and easy methods for evaluating fruit quality. Thus, the objective of this study was to estimate the quality attributes of Flame Seedless grape berries cultivated under various agronomical management and other practices using color space coordinates (berry L*, berry a*, and berry b*) as inputs in an artificial neural network (ANN) model with the best topology of (3-20-11). Satisfactory predictions based on the R2 range, which was 0.9817 to 0.9983, were obtained for physical properties (i.e., berry weight, berry length, and berry diameter as well as berry adherence strength) and chemical properties (i.e., anthocyanin, total soluble solids (TSS), TSS/titratable acidity, total sugars, titratable acidity, reducing sugars, and non-reducing sugars). Meanwhile, we also performed a contribution analysis to analyze the relative importance of CIELab colorimeter parameters of berries L*, a*, and b* to determine the main fruit quality. In terms of relative contribution, berry b* contributed relatively largely to berry weight, berry adherence strength, TSS, TSS/titratable acidity, titratable acidity, total sugars, reducing sugars, and non-reducing sugars and a* contributed relatively largely to anthocyanin, berry length, and berry diameter. The developed ANN prediction model can aid growers in enhancing the quality of Flame Seedless grape berries by selecting suitable agronomical management and other practices to avoid potential quality issues that could affect consumers of them. This research demonstrated how color space coordinates and ANN model may well be utilized to evaluate the Flame seedless grape berries’ quality.

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“…The computational confirmation of the constructed non-linear model was tested using standard statistical tests, such as relative absolute error (RAE), root mean square error (RMSE), and relative standard error (RSE). These commonly used parameters can be calculated as follows [42,43]:…”

Section: Performance Indicatorsmentioning

confidence: 99%

Prediction of the Form of a Hardened Metal Workpiece during the Straightening Process

et al. 2023

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In industry, metal workpieces are often heat-treated to improve their mechanical properties, which leads to unwanted deformations and changes in their geometry. Due to their high hardness (60 HRC or more), conventional bending and rolling straightening approaches are not effective, as a failure of the material occurs. The aim of the research was to develop a predictive model that predicts the change in the form of a hardened workpiece as a function of the arbitrary set of strikes that deform the surface plastically. A large-scale laboratory experiment was carried out in which a database of 3063 samples was prepared, based on the controlled application of plastic deformations on the surface of the workpiece and high-resolution capture of the workpiece geometry. The different types of input data, describing, on the one hand, the performed plastic surface deformations on the workpieces, and on the other hand the point cloud of the workpiece geometry, were combined appropriately into a form that is a suitable input for a U-Net convolutional neural network. The U-Net model’s performance was investigated using three statistical indicators. These indicators were: relative absolute error (RAE), root mean squared error (RMSE), and relative squared error (RSE). The results showed that the model had excellent prediction performance, with the mean values of RMSE less than 0.013, RAE less than 0.05, and RSE less than 0.004 on test data. Based on the results, we concluded that the proposed model could be a useful tool for designing an optimal straightening strategy for high-hardness metal workpieces. Our results will open the doors to implementing digital sustainability techniques, since more efficient handling will result in fewer subsequent heat treatments and shorter handling times. An important goal of digital sustainability is to reduce electricity consumption in production, which this approach will certainly do.

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Yield and Quality Prediction of Winter Rapeseed—Artificial Neural Network and Random Forest Models

Cited by 40 publications

References 64 publications

Prediction of Strawberries’ Quality Parameters Using Artificial Neural Networks

Prediction of Strawberries’ Quality Parameters Using Artificial Neural Networks

Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

Prediction of the Form of a Hardened Metal Workpiece during the Straightening Process

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