Evaluation of hawthorns maturity level by developing an automated machine learning-based algorithm

Azadnia, Rahim; Kheiralipour, Kamran

doi:10.1016/j.ecoinf.2022.101804

Cited by 19 publications

(5 citation statements)

References 42 publications

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“…The presence of redundant features could complicate the model development and data analysis. To this end, we used a quadratic sequential feature selection method (similar to [ 45 , 46 , 53 ] to identify and select optimum features for further analysis. The selected optimum features were used as inputs for the classification algorithms.…”

Section: Methodsmentioning

confidence: 99%

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Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios

Kheiralipour

Nadimi

Paliwal

2022

Sensors

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Rapid, non-destructive, and smart assessment of the maturity levels of fruit facilitates their harvesting and handling operations throughout the supply chain. Recent studies have introduced machine vision systems as a promising candidate for non-destructive evaluations of the ripeness levels of various agricultural and forest products. However, the reported models have been fruit-specific and cannot be applied to other fruit. In this regard, the current study aims to evaluate the feasibility of estimating the ripeness levels of wild pistachio fruit using image processing and artificial intelligence techniques. Images of wild pistachios at four ripeness levels were recorded using a digital camera, and 285 color and texture features were extracted from 160 samples. Using the quadratic sequential feature selection method, 16 efficient features were identified and used to estimate the maturity levels of samples. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and an artificial neural network (ANN) were employed to classify samples into four ripeness levels, including initial unripe, secondary unripe, ripe, and overripe. The developed machine vision system achieved a correct classification rate (CCR) of 93.75, 97.5, and 100%, respectively. The high accuracy of the developed models confirms the capability of the low-cost visible imaging system in assessing the ripeness of wild pistachios in a non-destructive, automated, and rapid manner.

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Section: Methodsmentioning

confidence: 99%

“…The performances of discriminant-based classifiers (i.e., LDA and QDA) were examined using CCR and MSE measures. Ultimately, the CCR measure was utilized to compare the performance of LDA, QDA, and the optimum ANN model [ 42 , 45 , 46 , 53 ].…”

Section: Methodsmentioning

confidence: 99%

Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios

Kheiralipour

Nadimi

Paliwal

2022

Sensors

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“…The dry fruits were taken in various lighting conditions and backgrounds, namely, artificial light and natural light, while the backgrounds included white, black, green, and human palms. The look of dry fruits, which also impacts their marketability, can be used to judge their quality to a large extent [4] . Although there are many datasets on fruits and vegetables, those working on machine learning models and/or apps need a dataset on dry fruits due to the numerous health benefits they offer [ 1 , 2 , 3 , 10 ].…”

Section: Data Descriptionmentioning

confidence: 99%

Dry fruit image dataset for machine learning applications

Meshram,

Choudhary,

Kale

et al. 2023

Data in Brief

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“…Machine learning by applying statistical and artificial methods is used to predict and classify the data extracted from different images 12 , 13 . Among them, artificial neural network and support vector machine are mainly used in classification of images 26 , 27 . So the aim of the present study is to employ digital thermal imaging coupled with machine learning to diagnose the potato dry rot in different disease progress levels.…”

Section: Introductionmentioning

confidence: 99%

A machine learning system to identify progress level of dry rot disease in potato tuber based on digital thermal image processing

Farokhzad,

Modaress Motlagh,

Ahmadi Moghaddam

et al. 2024

Sci Rep

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This study proposed a quick and reliable thermography-based method for detection of healthy potato tubers from those with dry rot disease and also determination of the level of disease development. The dry rot development inside potato tubers was classified based on the Wiersema Criteria, grade 0 to 3. The tubers were heated at 60 and 90 °C, and then thermal images were taken 10, 25, 40, and 70 s after heating. The surface temperature of the tubers was measured to select the best treatment for thermography, and the treatment with the highest thermal difference in each class was selected. The results of variance analysis of tuber surface temperature showed that tuber surface temperature was significantly different due to the severity of disease development inside the tuber. Total of 25 thermal images were prepared for each class, and then Otsu’s threshold method was employed to remove the background. Their histograms were extracted from the red, green, and blue surfaces, and, finally, six features were extracted from each histogram. Moreover, the co-occurrence matrix was extracted at four angles from the gray level images and five features were extracted from each co-occurrence matrix. Totally, each thermograph was described by 38 features. These features were used to implement the artificial neural networks and the support vector machine in order to classify and diagnose the severity of the disease. The results showed that the sensitivity of the models in the diagnosis of healthy tubers was 96 and 100%, respectively. The overall accuracy of the models in detecting the severity of tuber tissue destruction was 93 and 97%, respectively. The proposed methodology as an accurate, nondestructive, fast, and applicable system reduces the potato loss by rapid detection of the disease of the tubers.

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Evaluation of hawthorns maturity level by developing an automated machine learning-based algorithm

Cited by 19 publications

References 42 publications

Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios

Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios

Dry fruit image dataset for machine learning applications

A machine learning system to identify progress level of dry rot disease in potato tuber based on digital thermal image processing

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