Models for predicting the mass of lime fruits by some engineering properties

Ashtiani, Seyed-Hassan Miraei; Motie, Jalal Baradaran; Emadi, Bagher; Aghkhani, Mohammad Hossein

doi:10.1007/s13197-012-0862-1

Cited by 18 publications

(6 citation statements)

References 20 publications

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“…Power model based on minor diameter of apricot was found best as reported by Naderi‐Boldaji et al (). Miraei Ashtiani, Baradaran Motie, Emadi, and Aghkhani () suggested the application of a linear equation based on minor diameter for predicting the mass of lime ( M = 2.017c−43.868, R 2 = 0.97). Based on model selection criteria, nonlinear quadratic model based on geometric mean diameter of ungraded fruits may be recommended for kinnow fruit mass prediction as compared to other models.…”

Section: Resultsmentioning

confidence: 99%

“…In practice, the process of computing actual fruit volume is cumbersome and time‐consuming. Therefore, the models based on oblate spheroid ( V osp ) and ellipsoid ( V ellip ) that needs sample dimensions are preferred for the design of sorting equipment (Miraei Ashtiani et al, ). The power model based on “ V ellip ”(Equation ) was found suitable with maximum R 2 of 0.933 and lower χ 2 and RMSE of 22.74, and 4.39 for Grade 1 fruits, respectively.…”

Section: Resultsmentioning

confidence: 99%

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Mass modeling of kinnow mandarin based on some physical attributes

Mahawar

Bibwe

Jalgaonkar

et al. 2019

J Food Process Engineering

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The correlation between the physical properties of fruits such as their dimensions, projected areas, volume, and mass may assist in predicting fruit quality along with the development of post-harvest machinery. Thus, the present study aims to predict the mass of kinnow mandarin (Citrus reticulata L.) fruit as a function of its axial dimensions, projected areas, and volume using linear and nonlinear mathematical models (quadratic, power, and s-curve). Further, the mass models were presented under three different classifications: dimension based, projected area based, and volume based. The effect of size grading was also evaluated and compared with the data of ungraded fruits. Results showed that mass modeling based on dimensions and volume of ungraded fruits was more appropriate compared to individual grades. The quadratic model based on geometric mean diameter (R 2 = 0.956) and ellipsoid volume (R 2 = 0.955) are recommended for predicting the mass of ungraded fruits with maximal accuracy. Practical applicationMass based fruit grading is one of the important aspects of packaging as it not only reduces the wastage of handling and transportation resources by optimizing packaging formations but also enhances the marketability of commodity. Consumers generally prefer the fruits of uniform size, weight, and shape. Grading of horticultural produce is usually based on its appearance, size, and weight. The automatic fruit grading techniques generally use mass as a grading parameter due to its accuracy and effectiveness of the operation. The available kinnow grading systems primarily grade the fruits based on their dimensional attributes. Hence, the study was aimed at mass modeling of kinnow mandarin based on the selected engineering attributes such that results might be helpful to develop an accurate automatic grading system for grading based on the combined approach of size and mass. This study provides information about relationships between fruit mass and axial dimensions, projected areas, and volume, which are useful for the development of mass, and size based kinnow grading systems.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Mass modeling of kinnow mandarin based on some physical attributes

Mahawar

Bibwe

Jalgaonkar

et al. 2019

J Food Process Engineering

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“…Thus, numerous models have been prepared to predict physical attributes from the measurement of other attributes. Models for predicting fruit mass based on the measurement of physical features have been previously published [10][11][12][13][14][15][16][17][18][19][20]. All of the available models were developed based on a combination of different physical attributes in different forms using statistical approaches, such as regression analysis.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network and support vector regression in predicting mass of ber fruits (Ziziphus mauritiana Lamk.) based on fruit axial dimensions

2021

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Fruit quality attributes are important factors for designing a market for agricultural goods and commodities. Support vector regression (SVR), MLR, and ANN models were established to predict the mass of ber fruits (Ziziphus mauritiana Lamk.) based on the axial dimensions of the fruit from manual measurements of fruit length, minor fruit diameter, and maximum fruit diameter of four ber cultivars. The precision and accuracy of the established models were assessed given their predicted values. The results revealed that using the validation dataset, the developed ANN (R2 = 0.9771; root mean square error [RMSE] = 1.8479 g) and SVR (R2 = 0.9947; RMSE = 1.8814 g) models produced better results when predicting ber fruit mass than those obtained by the MLR model (R2 = 0.4614; RMSE = 11.3742 g). In estimating ber fruit mass, the established SVR and ANN models produced more precise prediction values than those produced by the MLR model; however, the performance differences between the SVR and ANN models were not clear.

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“…A result of this study shows that as the level of damage increased, the green spot index also increases. The authors (Ashtiani, Motie, Emadi, & Aghkhani, 2014) have considered different physical properties of lime using three image projections. The mathematical models have been evolved for predicting the lime mass in three different categories using dimensions, projected area and the volume assuming ellipsoidal and spheroid shape.…”

Section: Introductionmentioning

confidence: 99%

Clustered ANFIS weighing models for sweet lime (Citrus limetta) using computer vision system

Phate

Ramanathan

Palanisamy

2019

J Food Process Engineering

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In the present research work, a clustered adaptive neuro‐fuzzy inference system (ANFIS) is used to develop the model of relationship between the weight with 1D and 2D features of the Indian sweet lime fruit extracted through the developed computer vision system. Initially, three clustering methods are used to classify the input data into a homogeneous group. Then, the self‐learning proficiency of an artificial neural network and the ability of fuzzy theory to deal with the uncertainty and nonlinearity are utilized by developing the ANFIS model. Finally, the clustered data is combined with the ANFIS models trained by two different learning methods are analyzed. The goodness‐of‐fit indicators close to unity and the lower values of the error performance metrics implies the adequacy, robustness, and superiority of the proposed method. Moreover, the lower value of Theil's coefficient for uncertainty (UII) signifies the credibility of the model to predict the weight of a sweet lime fruit. Practical applications The presented improved approach of clustered ANFIS modeling for weighing sweet lime fruit will be fruitful to the researchers, farmers, and engineers. In order to, design and develop the real time weight‐based grading, sorting, and packaging machine's knowledge about the fruit weight‐size relation is a basic requirement. Use of this proposed approach (computer vision linked with the clustered ANFIS model) will greatly reduce the handling of the fruit as well as the workload that are responsible for the qualitative and quantitative losses of fruits on bulk industrial scale. Also, this weighing model using 1D and 2D features will be more beneficial, faster, and economically viable substitute to the on‐field manual method used in agriculture specially during the sorting, grading, and packaging of sweet lime.

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Models for predicting the mass of lime fruits by some engineering properties

Cited by 18 publications

References 20 publications

Mass modeling of kinnow mandarin based on some physical attributes

Mass modeling of kinnow mandarin based on some physical attributes

Application of artificial neural network and support vector regression in predicting mass of ber fruits (Ziziphus mauritiana Lamk.) based on fruit axial dimensions

Clustered ANFIS weighing models for sweet lime (Citrus limetta) using computer vision system

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