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چکیده
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Rapid, non-destructive, and smart assessment of the maturity levels of fruit facilitates their har-vesting 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 fruits. In this regard, the current study aims to eval-uate the feasibility of estimating the ripeness levels of wild pistachio fruit using image pro-cessing and artificial intelligence techniques. Images of wild pistachios at four levels of ripeness were recorded using a digital camera, and 285 color and texture features were extracted from 160 samples. Using quadratic sequential feature selection method, 16 efficient features were identi-fied 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 ca-pability 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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