Prediction of Pistachio (Pistacia vera L.) Mass Based on Shape and Size Attributes by Using Machine Learning Algorithms

Sağlam, Cevdet; Çetin, Necati

doi:10.1007/s12161-021-02154-6

Cited by 22 publications

(12 citation statements)

References 50 publications

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“…Random forest (RF) algorithm generates more than one decision tree with the use of bootstrap samples from the original training data to train each tree and is a good separator. Gaussian processes (GP) are of great significance in statistical modeling since all parameters inherited from a normal distribution could clearly be obtained (Sağlam & Çetin, 2021).…”

Section: Introductionmentioning

confidence: 99%

Machine learning algorithms to estimate drying characteristics of apples slices dried with different methods

Sağlam

Çetin

2022

Food Processing Preservation

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In this study, three different apple cultivars were dried using five different drying methods and moisture ratio (MR), moisture content (MC) and drying rate values were determined. Then, different machine learning algorithms (artificial neural network, k‐nearest neighbors, random forest, gaussian processes, and support vector regression) were used to estimate MR, MC and drying rate. For MR estimation of Golden Delicious, Oregon Spur and Granny Smith cultivars, Random Forest was most successful algorithm with correlation coefficients (R) of 0.9800, 0.9873, and 0.9841, respectively. This was followed by SVR with R: 0.9323 for Golden Delicious, ANN with R: 0.9766 for Oregon Spur and 5‐NN with R: 0.9827 for Granny Smith. MC and drying rate estimation results showed that RF, SVR, and k‐NN achieved higher R for all cultivars. It was concluded that machine learning algorithms are an effective approach for the accurate estimation of the drying characteristics of apple slices. Practical applications Machine learning‐like precise modeling techniques are used to estimate the drying characteristics of agricultural commodities. Models should be assessed and compared for optimization of drying conditions and operational costs. Machine learning models predictions agreed well with testing data sets and they could be useful for understanding and controlling the factors affecting drying behaviors.

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Section: Introductionmentioning

confidence: 99%

Machine learning algorithms to estimate drying characteristics of apples slices dried with different methods

Sağlam

Çetin

2022

Food Processing Preservation

Self Cite

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“…Random forests are an ensemble machine learning method that has been proposed by Breiman (2001) [20], which can get over the instability and overfitting problems when only a single decision tree is used [21]. When working on regression and classification mode, the random forest generates more than one decision tree using bootstrap samples of the original training data to develop and train each decision tree [22]. Therefore, the random forest involves developing different decision trees using random subsets of the original training data [21].…”

Section: Random Forestmentioning

confidence: 99%

Machine Learning to Predict the Adsorption Capacity for Microplastics

Astray¹,

Soria²,

Barreiro³

et al. 2023

Preprint

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Nowadays, there is extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through degradation processes of the plastics themselves, can contaminate the ecosystem with micro and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) to predict different microplastic/water partition coefficients (log Kd) were developed using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients upper than 0.92 in the query phase, which indicate that these type of models could be used for a rapid estimation of the absorption of organic contaminants on microplastics.

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“…The machine vision technology is able to visually acquire the geometric shape and other characteristics of the sample, and provided a noncontact automatic inspection method for grading agricultural products. This technology has the advantages of real‐time, high efficient and objective (Amraei, Mehdizadeh, & Nääs, 2018; Rong, Rao, & Ying, 2017; Saglam & Cetin, 2021; Vidyarthi, Tiwari, & Singh, 2020). Katrin, Marcus, Simone, et al (2019) extracted the geometric dimensions of mangoes using the image processing technology, estimated the fruit mass with the characteristics using length, width and thickness as the input parameters of the ANN, and compared different image processing methods.…”

Section: Introductionmentioning

confidence: 99%

Mass detection of walnut based on X‐ray imaging technology

Gao

Zhang

Sun

et al. 2022

J Food Process Engineering

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In view of the problem of low accuracy in walnut mass detection caused by relatively inconstant density, this study suggested the integration of X‐ray imaging technology and image processing technology with machine learning for walnut mass detection. Using image processing technology to remove the background of walnut X‐ray image and to segment the kernels, the mass prediction models could be constructed after extracting the shape and texture of walnut characteristic parameters and the kernel shape characteristic parameters. The results revealed that when mass prediction models were built based on walnut shape characteristic parameters or constructed with the application of texture characteristic parameters, the determination coefficient (R2) were rather low, even the residual predictive deviation (RPD) less than 1.4, proving that these models were not reliable. However, when mass prediction models were built based on walnut and kernel shape characteristic parameters, the R2 for both partial least squares (PLS) and radial basis function (RBF) models were higher than 0.84. And the RPD values were 1.8133 and 1.7474, respectively. On this basis, when competitive adaptive reweighed sampling (CARS) optimized parameters were adopted, the R2 for both PLS and RBF models were higher than 0.86 and the RPD were 1.8759 and 1.8850, respectively. The result proved that the application of walnut and kernel shape characteristics can improve the accuracy of model prediction. Therefore, using X‐ray imaging technology to detect walnut mass was feasible, which could realized the prompt, accurate and nondestructive detection of walnut mass. This technology provided a novel thought for the accurate grading of walnuts. Practical Applications At present, there is a problem of similar size but large mass difference after walnut size classification, which directly affects the commodity value of walnuts. It is necessary to combine multiple characteristics to select and classify walnuts. Mass is an important grading index. With the development of machine vision technology, online detection of fruit mass has been realized. However, due to the relatively inconstant density of walnuts and the limitation of the technology, the machine vision technique cannot obtain internal information. This study used X‐ray transmission to obtain the internal information of walnuts without destroying the walnuts. At the same time, the mass detection models were established by combining the internal and external characteristics of the walnuts to achieve fast, accurate, and nondestructive testing of walnut mass. Automatic mass detection provides technical support for intelligent grading, which has important production significance and economic value for the development of the walnut industry.

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Prediction of Pistachio (Pistacia vera L.) Mass Based on Shape and Size Attributes by Using Machine Learning Algorithms

Cited by 22 publications

References 50 publications

Machine learning algorithms to estimate drying characteristics of apples slices dried with different methods

Machine learning algorithms to estimate drying characteristics of apples slices dried with different methods

Machine Learning to Predict the Adsorption Capacity for Microplastics

Mass detection of walnut based on X‐ray imaging technology

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