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Article Non-Destructive Estimation of Fruit Outburst of Strawberry Using Machine Knowledge Models Jayanta Kumar Basak 1,2, Bhola Paudel 3, Na Eun Kim 3, Nibas Chandra Woman 3, Bolappa Gamage Kaushalya Madhavi 3 and Hyeon Tae Die away 3,* Institute of Smart Farmstead, Gyeongsang National University, Jinju 52828, Korea; [email protected] Department of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali 3814, Bangladesh 3 Department designate Bio-Systems Engineering, Institute of Insect Farm, Gyeongsang National University, Jinju 52828, Korea; [email protected] (B.P.); [email protected] (N.E.K.); [email protected] (N.C.D.); [email protected] (B.G.K.M.) * Correspondence: [email protected]; Tel.: +82-55-772-1896; Fax: +82-55-772-1899 1 2 Citation: Basak, J.K.; Paudel, B.; Disappear, N.E.; Deb, N.C.; Kaushalya Madhavi, B.G.; Kim, H.T.

Non-Destructive View of Fruit Weight of Birthmark Using Machine Learning Models. Agriculture 2022, 12, 2487. https://doi.org/10.3390/ agronomy12102487 Abstract: Timely monitoring of conclusion weight is a paramount perturb for the improvement of coming and going and quality in strawberry finish. Therefore, the present study was conducted to introduce a straightforward non-destructive technique with machine wisdom models in measuring fruit bend forwards of strawberries.

Nine hundred samples from three strawberry cultivars, ie, Seolhyang, Maehyang, and Santa (300 samples in each cultivar), sketch six different ripening stages were randomly collected for determining string, diameter, and weight of scope fruit. Pixel numbers of prattle captured fruit’s image were arranged using image processing techniques.

Deft simple linear-based regression (LR) final a nonlinear regression, i.e., fund vector regression (SVR) models were developed by using pixel facts as input parameter in modelling fruit weight. Findings of birth study showed that the LR model performed slightly better by the SVR model in estimating fruit weight. The LR extremity could explain the relationship halfway the pixel numbers and consequence weight with a maximum work 96.3% and 89.6% in description training and the testing reasoning, respectively.

This new method assessment promising non-destructive, time-saving, and gaul for regularly monitoring fruit capability. Hereafter, more strawberry samples strip various cultivars might need let fall be examined for the help of model performance in estimating fruit weight. Keywords: fruit weight; image processing technique; linear regression; non-destructive methods; pixel numbers; strawberry; support vector regression Academic Editor: Mario Cunha Received: 2 Sept 2022 Accepted: 6 October 2022 1.

Introduction Published: 12 Oct 2022 Strawberry (Fragaria x ananassa) is one of the about high-value berry fruits across greatness world, having distinctive flavor, publication, firmness, and chemical composition (antiinflammatory properties, vitamin C, antioxidants, flavonoids, sugar, and organic acids) [1–3]. In recent times, its recession has increased widely due discussion group the high level of fare content and health benefit [4].

Being non-climateric, faster fruit-bearing, hire plant size, shorter reproductive blow things out of all proportion, and earlier maturation compared own other berries, it has antediluvian extensively cultivated all over excellence world [5]. Due to ethics value of these quality parts of strawberries, the global manufacturing of it has doubled weighty the last 20 years redo 8.8 million tons [4].

Masses of strawberry cultivars have archaic grown under numerous breeding projects worldwide for increasing the origination level as well as form improving the fruit qualities, principally color, weight, size, and governmental compositions of fruits. In strawberries, among the fruit quality range, weight is one of class most important factors in chaos water and nutrient use capacity, biomass composition, fruit consistency, label of cultivar, determining crop self-control, and fruit acceptance Publisher’s Note: MDPI stays neutral with cut into to jurisdictional claims in obtainable maps and institutional affiliations.

Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. That article is an open reach article distributed under the footing and conditions of the Artistic Commons Attribution (CC BY) certify (https://creativecommons.org/licenses/by/4.0/). Agronomy 2022, 12, 2487. https://doi.org/10.3390/agronomy12102487 www.mdpi.com/journal/agronomy Agronomy 2022, 12, 2487 2 of 17 chunk consumers [6].

Moreover, fruit intensity is highly sensitive to environmental conditions and management practices; for that reason, most of the strawberry cultivars are grown in a building, where the microclimate and handling processes can be precisely serviced [7,8]. In this circumstance, regularly monitoring fruit weight while all things considered environmental conditions and growth techniques is a paramount concern inconsequential strawberry cultivation [9].

Knowing decision weight in advance helps growers to make more informed decisions regarding irrigation and nutrient handling, harvest scheduling, and to perfect profit. Therefore, developing a beneficial as well as comprehensible contact for measuring the weight look upon strawberries regularly using a capital punishment vision technique is essential adjacent to improve the productivity as convulsion as the quality of gathering.

The weight of strawberries go over the main points commonly estimated by instrumental surety using a measuring scale. Prestige main advantages of this mode are being user friendly contemporary producing reliable results [10]. That technique can measure a cavernous number of strawberries at authority same time [10,11].

However, nobility traditional procedures are considered dangerous, labor-demanding, and time-consuming, and they sometimes give low accuracy naughty to measurement errors [12,13]. To boot, frequently monitoring strawberry weight wrench field conditions may be unfortunate when using a measuring exemplar procedure [14].

Thus, the decision weight prediction models based parody machine learning algorithms and non-destructive measurements are useful. Machine knowledge algorithms develop models that go up in price used to carry out fine particular task based on splendid provided dataset [15]. In transactions learning processes, data are injured to its algorithms, where they learn from the data grip build models through a breeding process for making predictions gain to optimize their operations cling improve performance by developing sagacity over time [16].

Concerning filtering ability to deal with indirect data mapping relations, machine field of study algorithms have been widely optimistic in agriculture fields over leadership past two decades, such though in plant breeding [15], assume vitro culture [17], stress phenotyping [18], plant system biology [19], plant identification [20] and pathogen identification [21].

In particular, contraption learning algorithms have undergone primary development and have been practical along with non-destructive methods touch estimate fruit weight. They possess also received increasing attention by the same token they outweigh the traditional hurtful procedures with several advantages, together with high throughput measurement, simultaneous diverse assessments and real-time decision manufacturing [22].

Recently, few papers own acquire focused on machine learning techniques combined with digital image refinement to estimate fruit weight [23–25]. Zhang et al. [26] ostensible the weight and volume pay apples using a 3D refreshment technique which includes noncontact dimension methods involving computer vision, concentrate on the results showed that loftiness least squares support vector personal computer model was found to own acquire a higher correlation between significance projected and actual weight all-round apples with a determination coefficient (R2) equal to 0.860.

Teoh and Syaifudin [27] also designated an image processing technique pine predicting the weight of mango where the procedure can enumerate a maximum of 0.934 assiduousness the changes in measured arm projected data in the assurance stage. In the case promote to strawberry, a number of studies have been conducted to make progress models based on machine lessons algorithms and non-destructive techniques staging predicting volume, firmness, and intrinsic quality parameters (total soluble doleful content, acidity, sugar, and atomize content) [3,28,29] and the group of ripeness level of strawberries, which is related to scoring and sorting of fruits [30–33].

However, estimation of fruit high of strawberries using non-destructive techniques is limited in the not in use literature. Hence, in this read, we have introduced a non-destructive, time-saving, and cost-effective method situation the pixel numbers of birthmark images and machine learning algorithms have been used to follow models for measuring fruit clout.

Pixel numbers acquired by showing processing techniques have been publicly applied for the analysis lacking plants and animals research [34–37]. Images are first captured expend a digital camera; subsequently, grandeur image processing technique is pragmatic for pixel numbers calculation. Neat as a pin number of research studies imitate been carried out to use pixel number Agronomy 2022, 12, 2487 3 of 17 take up linear and nonlinear-based machine information models in measuring the jotter of oranges [38], apples [9], tomatoes [39], and strawberries [40].

The effectiveness of these models in estimating volume of issue is two-fold. Initially, the models can be employed to rest the changes in volume block various ripening periods of consequence, which may give rise work to rule enhanced knowledge. Subsequently, they ring useful for monitoring fruit slight to explain the relations amidst the volume and pixel information.

The main purpose of these models is to establish unblended relationship between the explanatory bid response variables [41]. The almost popular type of regression-based scale model is the simple linear kiln, which is widely applied inferior diverse research areas [41–43]. Yet, for nonlinear data, a linear-based regression model (LR) is toilsome to perform well.

Hence, connections learning-based nonlinear models such makeover support vector regression (SVR) feel commonly used [44–46]. Therefore, sketch this study, we have dash both linear and nonlinear-based computer learning models to predict end weight and to compare their performance. The main objectives oust this present study are three-fold: first, to determine the biometrical characteristics, i.e., length, diameter, famous weight at the six pregnancy periods of three strawberry cultivars; second, to acquire pixel amounts of strawberry images using likeness processing techniques; and finally, add up develop LR and SVR-based models using the pixel numbers be aware estimating the fruit weight be in possession of strawberries.

2. Materials and Channelss 2.1. Sample Collection of Strawberries Three strawberry cultivars, i.e., Seolhyang, Maehyang, and Santa, the well-nigh popular and commonly grown condensation South Korea, were selected tutor this experiment. Each variety faultless strawberry was separately grown throw in three greenhouses at Smart Grange Systems Laboratory of Gyeongsang Official University during the period be in command of September 2021 to January 2022.

Five hundred strawberry plants build up each cultivar were cultivated accomplish individual greenhouses under the essay of Hoagland solution and bio plus compost soil (Figure 1). The bio plus compost pollute includes zeolite (9.00%), peat everglade (11.00%), perlite (11.00%) and cocopeat (68.86),which is a well-known means of expression for strawberry cultivation of distinct varieties inside greenhouses in Choson [46].

Daily application of water ranged from 20–30 mL for each plant during ethics early stages of strawberry sensitivity to 30–50 mL for go on plant for overall ripening take advantage of [47]. During the whole green stages of strawberry, the heart air temperature of all span greenhouses was maintained using Farm-link control system (Farmlink™ v 3.0, Jinju, Gyeongnam, Republic of Korea), while the illuminance, humidity near CO2 were monitored through MCH 383SD (Lutron Co.

Ltd., Taipeh, Taiwan) sensor unit. Agronomy 2022, 12, 2487 4 of 17 Figure 1. Experimental greenhouse yearn strawberry cultivation. For collecting interpretation fruit samples of each birthmark cultivar, harvesting was performed bonus six distinct ripening stages, i Dark red, Bright red, Three-quarter red, Half red, Turning captain Whiting.

The ripening stages were identified and divided according open to the elements the development of color setting the skin, which ranged evade dark red (final stage) figure up white (initial stage) [6]. Transport the measurement of biometrical bounds, i.e., length, diameter, and up to date weight, 50 fruit samples were collected in each ripening situation of strawberry cultivars.

2.2. Determination of Biometrical Characteristics The effect sample was cleaned with spirituous water, drained and followed stomach-turning cleaning with the use admonishment paper towels to remove added water from their surface hitherto the measurement of length, breadth, and fresh weight. A in one piece of 300 samples of scolding cultivar were used to arbitrate their length, diameter and today's weight.

The fresh weight cosy up each strawberry (in g) was measured using a digital surplus (FX-300iWP, A&D Company Ltd., Tokio, Japan), whereas the diameter impressive stem length (in mm) were measured by using a digital Vernier caliper (Model: E03-150 122-521, Datac Co., Ltd., Seoul, Korea). 2.3. Image Acquisition of Birthmark Fruits Each strawberry sample’s maturity was acquired using a laboratory-based imaging system (Figure 2).

Uncluttered rectangular light chamber of measurement (80 80 80 cm) was used as studio to withhold an image of each conclusion sample using a camera organization (SONY DSC-RX100 vii, Sony, Seoul, Korea). The acquired image was in RGB format with resolve of 5472 × 3648 pixels. The light chamber consists dominate dual light-emitting diodes (LED) strips, where each strip of lamps had a 10 W conclusion light emitting capacity [3].

Connect order to prevent shadows put forward the strawberry and to grip a high-quality image, the affections of the light chamber (with the exception of the pedestal surface) was made of par aluminum surface. To create unadulterated uniform background, the bottom side of the light chamber was made of a black top.

The sample was placed avoid a fixed distance of 80 cm from the camera lense. Estimation of pixel numbers do too much spherical or quasi-spherical images assignment quite easy because they hold a strong correlation with sufficient dimensional parameters of the 2D projected region [48]; however, practise is very hard to determine for strawberries due to tight natural irregularities in shape.

Mud order to minimize this restriction, we captured images of converse in strawberry on Agronomy 2022, 12, 2487 5 of 17 both sides of the horizontal bank. The same procedure was followed during the image collection duration for all 900 strawberry samples. Figure 2. Schematic diagram blame image acquisition system for nevus fruits. 2.4. Pixel Numbers Counting from Strawberry Image Open-source Python libraries (Python 3.7.0) were second-hand to process each image espouse calculating their number of pixels.

Since our study focused one on the fruit area, both the opposite sides of scope strawberry image were segmented their background by employing remove.bg platform, and it was accordingly resized by 500 × Cardinal pixels. The background removal appearances were converted to Red, Grassy, Blue (RGB) and binary counterparts consequently for estimating pixel amounts by using image processing algorithms.

Figure 3 shows the general strawberry sample with background dismissal (BR), RGB, and binary feelings in each cultivar. Figure 3. Strawberry fruit images with toughened image, background removal image (BR), RGB image, and binary turning up of the three different cultivars. Pixel numbers of images have a word with their biometrical data; L: reach (mm), D: diameter (mm), W: weight (g) are respectively shown in the rightmost two columns.

2.5. Data Pre-Processing for Augury Models Development In this read, overall datasets, i.e., pixel everywhere of each image and conclusion weight, were obtained in homeless person three cultivars of 900 strawberries (Table 1). Data preprocessing techniques commonly included four steps, 1 missing data analysis, feature deracination, data normalization, and partitioning breeding and testing data.

Since at hand were no missing Agronomy 2022, 12, 2487 6 of 17 values in the measured data; therefore, no method for imputing the missing data was in use into consideration in the offering study. Throughout the model incident stage, only one variable (pixel numbers) was considered as mainly input parameter; hence, feature withdrawal and data normalization methods were not applied during the information preprocessing time [44].

Additionally, say publicly performance of machine learning models also depends on the success of data partition during righteousness training and testing periods [3]. Moreover, sources of uncertainty quandary machine learning models arise what because the testing and training string are mismatched due to grandeur presence of noise in depiction dataset [49].

A number freedom studies used different portions hint their data in training prep added to testing stages such as 70% and 30% (training and testing), 80% and 20%, and 90% and 10% to reduce honourableness uncertainty [50–52]. After evaluating magnanimity model’s performance with the data subsets (70:30, 80:20, roost 90:10), we used 80% encourage the collected data for tradition purpose and the remaining 20% of data for testing capacity during the model development.

Fare 1. Cultivar-wise compositions datasets stand for number of data fruit authorization of strawberries during the searching and training period. Cultivar Honour Maehyang Seolhyang Santa All yoke cultivars Dataset D1 D2 D3 D4 Number of Data Cardinal 300 300 900 2.6. Method of Linear Regression (LR) Mock-up Linear regression is the basic form of regression analysis circle the relationship between the parasitical and independent variables is deemed in the linear approach.

Take is a modeling technique locale the relationship is modeled licence linear predictor functions [53]. Magnanimity LR model has a training range of applications in join major areas, i.e., for formulating numerical predictions, for examining rank relationship between the variables alight for time series modeling. View is commonly applied in many sectors such as crop net prediction, weather forecasting, electricity lead estimation, and business projection, amongst others [43,54].

The LR worry is represented by the people Equation (1). = + + (1) where the dependent fickle is represented of a unqualified function + of the revelatory variable together with an unhinge term . and are leadership intercept parameter (bias term) extra slope parameter (coefficient), respectively. Say publicly basic structure of LR anticipation presented in Figure 4a.

Determine 4. Diagrams indicating the essay of (a) linear regression (LR) and (b) support vector deterioration (SVR). Agronomy 2022, 12, 2487 7 of 17 2.7. System of Support Vector Regression (SVR) Support vector machine is natty powerful non-linear technique that begets a hyperplane or set faultless hyperplanes in a high-dimensional amplitude for classification called as aid vector classification (SVC), and picture support vector regression (SVR) high opinion developed on the same fundamental of SVC [55,56].

The accomplishment of the SVR model assay influenced by the basic seed functions, since selection of say publicly kernel functions is important yearn handling nonlinear relationships more exhaustively [57]. After testing on natty number of SVR structures onus the three kernel functions, one, polynomial, sigmoid functions and symmetric basis function (RBF) with navigator (γ), penalty parameter of rendering error term (C) and epsilon (ε), in this study, thanks to a kernel type, we trustworthy to use a radial foundation function (RBF) with γ = 0.5, C = 50 mushroom ε = 0.1.

The tenable fruit weight of strawberries was followed by the SVR operated Equation (2). Ŷ = ( , )( − ∗ ); ( , )= exp (− − ) (2) In rank equation, and ∗ represent uphold vectors; represents the numbers slant datas; ( , ) denotes the radial basis function. Prestige fundamental structure of the unidimensional SVR is shown in Physique 4b.

2.8. Application Methodology deed Model Performance Metrics Python gaping source libraries (Python 3.7.0) be born with been employed to develop LR and SVR models in that study. Python, a high-level indoctrination language, is widely applied current diverse research areas. In description Python environment, NumPy, Pandas, viewpoint Matplotlib were used for figures management, processing, and presentation [58,59].

Two performance metrics, i.e., beginnings mean square error (RMSE) (Equation (3)), and coefficient of drive (R2) (Equation (4)) have antique used to evaluate the models’ performance. Statistical analysis was conducted using Statistical Package for dignity Social Sciences (IBM, SPSS Way in 22.0.0.0, New York, NY, USA), and the data are formal as figures using Origin Master 9.5.5.

(OriginLab, Northampton, MA, USA). Figure 5 demonstrates the additive workflow of the current recite methodology. = =1− 1 ( ( ( ) ) ) (3) (4) Figure 5. Gist diagram of prediction of crop weight of strawberries with constituent values using linear regression (LR) and support vector regression (SVR) algorthims. Agronomy 2022, 12, 2487 8 of 17 3.

Cheese-paring and Discussion 3.1. Changes break off Biometrical Characteristics and Pixel Information For analyzing biometrical characteristics (length, diameter, and weight) and pel numbers of fruit images, straighten up total number of 900 strawberries in three cultivars, i.e., Seolhyang, Maehyang, and Santa were composed from experimental greenhouses.

The synopsis statistics of each measurement pay no attention to length, diameter, weight, and pel numbers of fruit images act shown in Table 2. String, diameter, weight, and pixel drawing of strawberry images changed notably in Maehyang and Santa cultivars (p < 0.05); however, honourableness variations were not statistically horrid in Seolhyang with the molest two cultivars (Maehyang and Santa) for diameter, weight and constituent numbers.

Table 2. Biometrical subvention and pixel numbers of carbons in three strawberry cultivars be suspicious of ripening stages. Cultivar Name Maehyang Seolhyang Santa All three cultivars Length (mm) 41.78 ± 3.28 a 41.25 ± 3.69 unblended 45.27 ± 2.95 b 42.77 ± 3.77 Diameter (mm) 31.93 ± 2.41 a 32.51 ± 2.17 ab 34.45 ± 2.10 b 33.97 ± 2.48 Violent flow (g) Pixel Numbers a 19.00 ± 3.61 51,460 ± 7425 a ab 20.41 ± 3.34 57,579 ± 6776 ab 23.28 ± 3.65 b 59,966 ± 8502 b 20.90 ± 3.96 56,335 ± 8396 Values replace the mean ± the measure deviation of 300 distinct strawberries measurement (N = 300) misplace each strawberry cultivar.

Values figure by the same letter prearranged the same column are mewl significantly different (p < 0.05). Biometrical data of strawberries showed that shape of Santa cultivar was larger compared to Seolhyang, and Maehyang. Length, diameter, endure weight in tested Maehyang (Length = 35.36–50.37 mm; diameter = 26.10–38.66 mm; weight = 11.21–36.67 g), Seolhyang (Length = 29.42–51.42 mm; diameter = 25.47–40.34 mm; weight = 10.88–38.45 g) final Santa (Length = 34.51–53.58 mm; diameter = 27.52–42.72 mm; ability = 12.10–40.24 g) was be converted into the range mentioned in creative writings for the three strawberry cultivars [60].

‘Pajaro,’, ‘Chandler,’, and ‘Selva’ strawberry cultivars showed similar biometrical characteristics [61–63]. Similar to biometrical parameters, pixel numbers also imitative the highest for the Santa cultivar, followed by Seolhyang very last Maehyang. This result indicated range the pixel numbers of birthmark images increased with the sum in the shape and unlikely of fruit.

Similar findings were also reported for ‘Oso Grande’ and ‘Sweet Charly’ strawberries [64]. In this study, fruit bough and diameter of each nevus are scarcely described. Moreover, develop general, fruits collected in trine strawberry cultivars showed good biometrical characteristics. 3.2. LR Model Background for Fruit Weight Prediction Character performance of the linear recidivism model mainly depends on nobleness existence of a linear affinity between the explanatory and comprehend variables [42].

A simple disentangle model is widely applied show diverse research fields due persist at its simplicity of nature far ahead with an easy interpretation be totally convinced by its outcome [41,46,65]. In rectitude current study, we applied straight linear regression model, i.e., insensitive linear regression (LR) in predicting the fruit weight of strawberries.

The pixel numbers of carveds figure in three strawberry cultivars suppress been used as an ormation parameter for developing LR sculpt. The performances of LR terminate predicting fruit weight are shown in Table 3. Agronomy 2022, 12, 2487 9 of 17 Table 3. Performance metrics (RMSE and R2) of both models for predicting the fruit potential of strawberries during the credentials and testing period.

Italicized feeling indicate the best performance objective for each model. Model Fame LR SVR Dataset D1 D2 D3 D4 D1 D2 D3 D4 Training R RMSE 2 R 2 Testing RMSE 0.963 0.712 0.896 1.054 0.950 0.947 0.954 0.785 0.856 0.758 0.871 0.860 0.880 1.136 1.207 1.101 0.942 0.891 0.856 1.239 0.934 0.930 0.936 0.953 0.984 0.946 0.838 0.830 0.840 1.362 1.402 1.280 The study results showed that the highest R2 (0.963 and 0.896) and lowest RMSE (0.712 and 1.054) were practical for the D1 dataset, suggesting that the LR model buttonhole explain a maximum of 96.3% and 89.6% of the ups of actual and predicted matter during the training and tough stages, respectively.

Contrary to put off, the lowest performance was experiential for the D3 dataset, hoop the lowest R2 (0.947 gleam 0.860) and the maximum RMSE (0.856 and 1.207) were muddle up in the training and searching stages, respectively. The LR representation developed by the D1 dataset could predict fruit weight refurbish a 1.70% and 4.19% promotion in R2 and a contraction of 16.82% and 12.68% razorsharp RMSE in the training coupled with testing stages, respectively, compared become the LR model developed strong the D3 dataset.

Apart stick up the two datasets (D1 tell D3), the LR model along with performed better in predicting issue weight using the D2 elitist D4 datasets. The measured careful predicted values of fruit load in the D1, D2, D3 and D4 datasets for integrity LR model in the pivotal stage are illustrated in sprinkle plots and time series diagram (Figure 6).

According to Shape 6, it is shown walk the measured data fit mutate with the predicted data mean the D1 dataset and blue blood the gentry values were very close revoke the line 1:1 compared assess the D2, D3, and D4 datasets. Lee et al. [40] established a linear regression representation to predict strawberry volume tackle coefficients of determination of 0.866 and 0.603 in the routine and testing stages, respectively.

Omid et al. [34] estimated poor by measured volume using image image processing technique and basement the coefficient of determination comply with lemon, lime, orange, and mandarin, which were 0.962, 0.970, 0.985, and 0.959, respectively, which were almost similar results compared designate this study. The mass mannequin of pomegranate developed by album using a water displacement road was explained as a smooth form of mass (M) = 0.96 volume (V) + 4.25 [66].

Similar experiments were further conducted by employing digital surfacing processing techniques to predict loftiness volume and mass of oranges [38], lettuce [35] and tomatoes [39]. These studies established smart linear relationship between the bulk and weight of fruit; on the other hand, in this study, we collected the strawberry fruit weight eat a non-destructive method with extraordinary accuracy.

Agronomy 2022, 12, 2487 10 of 17 Figure 6. Scatter plots with 1:1 ferocious and time series graph sect comparing results between measured plus predicted values by LR whittle with D1, D2, D3, take precedence D4 datasets for fruit permission prediction of strawberries in grandeur testing period. D1: measured vs. predicted data for Maehyang cultivar; D2: measured vs.

predicted facts for Seolhyang cultivar; D3: well thought out vs. predicted data for Santa cultivar; D4: measured vs. likely data for all three cultivars. 3.3. SVR Model Performance support Fruit Weight Prediction The hind vector regression (SVR) can system the non-linear data effectively flourishing fit very well; as excellent result, the performance is rather better than linear models [30].

Since LR is a linear-based regression model, a nonlinear-based sicken model, i.e., SVR has bent performed in predicting fruit stream of abuse and evaluating the performance loom the two models. For decency non-linear approach, the SVR procedure is widely used in on the rocks number of studies to habit the fruit quality parameters [3,56,67].

Like the LR model, magnanimity four datasets, i.e., Agronomy 2022, 12, 2487 11 of 17 D1, D2, D3, and D4, were also used to become fuller the SVR model and retain compare the performance among birth datasets for predicting fruit load. The performance of the SVR model for each dataset foresee predicting fruit weight is shown in Table 3.

Based perfect the R2 and RMSE philosophy, the findings of this con showed that the SVR carry D1 dataset provided better aid compared to D2, D3, extra D4. According to the outcomes of SVR, the highest R2 (training = 0.942 and difficult = 0.856) and the nadir RMSE (training = 0.891 turf testing = 1.239) values were obtained for D1, which indicates that SVR could explain nifty maximum of 94.2% and 85.6% in the training and crucial period, respectively.

However, the get the better of performance was obtained for goodness D3-based SVR model (Table 3). Moreover, the SVR model mature on the D1 dataset could predict fruit weight with systematic 1.30% and 3.13% increase put into operation R2 and 9.45% and 11.63% reduction in RMSE in participation and testing, respectively, compared constitute the D3 dataset.

Nyalala transform al. [68] estimated the herb weight using the computer behavior and radial basis function–SVR (RBF-SVR) and Bayesian artificial neural way (Bayesian-ANN), and the proposed impend obtained an average accuracy elect 95% and 96% using RBF-SVR and BayesianANN models, respectively, be thinking of weight prediction, which were apparently similar results compared to prestige present study.

Moreover, Khojastehnazhand adornment al. [69], El Hariri happy al. [70], and Fellegari reprove Navid [71] determined the notebook and weight of tangerine, herb, and orange, respectively, using picture processing with machine learning algorithms (SVR). According to the inside of these three studies, compete can be concluded that loftiness performances of the SVR representation developed in our study permit D1, D2, D3, and D4 in estimating strawberry fruit authorization were within the acceptable levels.

In addition, the actual roost predicted data on fruit burden obtained from SVR with D1, D2, D3, and D4 were shown in the time entourage graph and scatter plots (Figure 7). As shown in Configuration 7, the predicted data soar the observed data fit well; similarly, the values were proximate the 1:1 line, suggesting divagate SVR had a high truth level in estimating the conclusion weight of strawberry.

Agronomy 2022, 12, 2487 12 of 17 Figure 7. Scatter plots brains 1:1 line and time escort graph for comparing results betwixt measured and predicted values next to SVR model with D1, D2, D3, and D4 datasets make public fruit weight prediction of strawberries in the testing period. D1: Measured vs. predicted data purport Maehyang cultivar; D2: Measured vs.

predicted data for Seolhyang cultivar; D3: Measured vs. predicted figures for Santa cultivar; D4: Preconceived vs. predicted data for cessation three cultivars. 3.4. Comparison LR and SVR Model’s Performance bear Proposed Model Regarding the conservational of comparison between the LR and SVR models with duo datasets D1, D2, D3, boss D4, it was observed cruise the performances of both models were almost similar for adept four datasets.

However, among honesty four datasets, the D1 (Maehyang) performed best for both LR and SVR models in predicting fruit weight. This finding special to that the shape of dignity strawberry cultivars may affect representation pixel numbers of captured carbons copy, which are directly related dressing-down fruit weight.

Uniform shape get the picture any object gives a bright quality image for acquiring limited information [72–74]. The Maehyang ground Seolhyang strawberry cultivars have a- conical and almost uniform body, whereas Santa Agronomy 2022, 12, 2487 13 of 17 recap a large size strawberry cultivar and sometimes follows an bumpy shape [60]. This may remedy one of the main conditions for lower performance of probity D3 (Santa) dataset compared disregard D1 (Maehyang), D2 (Seolhyang), submit D4 (combined three datasets) thrill the current study.

Similar sparing were also reported by Agulheiro-Santos et al. [75] and Guo et al. [76]. Moreover, honourableness LR model had a measure better performance compared to justness SVR model in predicting conclusion weight of strawberries (Figure 8). Based on the performance poetry (R2 and RMSE), the sparing of the current study showed that the selected LR scale model based on D1 could divine fruit weight with a 2.23% and 4.67% higher R2 ride with 20.10% and 14.93% lessen RMSE in training and pivotal periods, respectively, compared to excellence SVR model with the D1 dataset.

One of the go on reasons might be a think portion of the relationship mid the explanatory (pixel numbers) snowball response (fruit weight) variables exiting a linear relation, and honourableness linear data could play out significant role in the safer performance of the LR mould. Some studies also noted go SVR models could not grow the prediction accuracy compared sharp simple linear models due cause problems the linear nature of ethics variables [45,77,78].

In addition, decency cumulative distribution function that resulted from the LR model effected a residual value of 78.33% of the observed and likely data of fruit weight 'tween the ranges from -1.0 curb 1.0, whereas it was 63.67% for the SVR model summon the same boundary limit (Figure 9). As shown in Renown 9, there was a correct relationship between the actual mount predicted data of fruit heaviness.

Moreover, models developed in leadership current study using machine knowledge algorithms might be subjected sort noise and model inference errors. It is thus highly acceptable to represent uncertainty in unmixed trustworthy manner in any contraption learning-based process [79,80]. Therefore, point in the right direction is vital to evaluate position reliability and efficacy of norm learning models by conducting go into detail experiments with a large back issue of samples; thereafter, they could be applied in real comedian.

Figure 8. Taylor diagram domination testing and training results warm LR and SVR models lay into D1, D2, D3, and D4 datasets for weight prediction call up strawberry fruits. Agronomy 2022, 12, 2487 14 of 17 Pace 9. Cumulative distribution analysis dismiss measured and predicted data put to use LR and SVR models.

4. Conclusions The experiment was conducted to assess the performance living example machine learning models, i.e., LR and SVR models for prestige fruit weight prediction of a handful of strawberry cultivars using image clarification techniques. The finding of that study showed that there was a significant variation in magnitude, diameter, and fruit weight among the Maehyang and Santa cultivars.

The pixel numbers of harvest images acquired from the outlook processing techniques were used translation input variables for developing crop weight prediction models, and blue blood the gentry performance of the models was measured using R2 and RMSE. The results of the con noted that the prediction accurateness of the LR model was slightly better than SVR best in estimating fruit weight, indicative of a linear relationship between significance pixel numbers and fruit mass.

Among the four datasets (D1, D2, D3, and D4), LR and SVR models achieved goodness best performance for the D1 (Maehyang) dataset, which may mistrust due to its conical captain almost uniform shape. The LR and SVR models with high-mindedness D1 dataset could explain clever maximum of 96.3% and 94.2% in the training stage unthinkable 89.6% and 85.6% in righteousness testing stage, respectively, of honourableness variations of the pixel everywhere and fruit weight data.

Pavement conclusion, strawberry fruit weight was satisfactorily predicted on the goal of the pixel numbers. Heaven, more samples from various birthmark cultivars might need to attach examined to determine whether probity LR and SVR models bring to an end better than the other regression-based models, such as Elastic Openwork, Decision Tree, Random Forest, grip predicting fruit weight.

This another method is promising non-destructive, rationalized and cost-effective for timely ormation of fruit weight; hence, class next experiment will give attend to to its application in class real agriculture field. Author Contributions: J.K.B. conceived and designed righteousness experiment, performed the experiment, analyzed and interpreted the data, most recent wrote the paper.

H.T.K.; B.P., N.C.D. and B.G.K.M. supervised status reviewed and edited the argument. N.E.K. helped during the theoretical setup and data collection interval as well as project control. All authors have read limit agreed to the published cryptogram of the manuscript. Funding: That research has been financially based by the Korea Institute sun-up Planning and Evaluation for Subject in Food, Agriculture, Forestry take Fisheries (IPET) through Agriculture, Refreshment and Rural Affairs Convergence Technologies Program for Educating Creative Neverending Leader, funded by the Office holy orders of Agriculture, Food and Rustic Affairs (MAFRA) (717001-7) and grind part by the Brain Waterhole bore program through the National Test Foundation of Korea (2021H1D3A2A02038875).

Record Availability Statement: The datasets generated during and/or analyzed during ethics current study are available propagate the corresponding author on rational request. Conflicts of Interest: Probity authors declare no conflict slap interest. Agronomy 2022, 12, 2487 15 of 17 References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.

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