roya farhadi; Amir Hossein Afkari Sayyah; Bahareh Jamshidi; ahmad mousapour gorji
Abstract
Potato storage is essential to ensure the continued supplying of potatoes to consumers and the potato processing industry. During storage, physiological changes and water loss lead to changes in color, shape, size, and texture of potatoes. Therefore, there is a need for a quick and accurate method to ...
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Potato storage is essential to ensure the continued supplying of potatoes to consumers and the potato processing industry. During storage, physiological changes and water loss lead to changes in color, shape, size, and texture of potatoes. Therefore, there is a need for a quick and accurate method to measure the quality of the product. In this study, machine vision and neural network methods were used in classification and modeling of two stored potato samples (Agria and Clone 8-397009) under constant and variable conditions. Among 29 measured parameters relating to color, texture and morphological features of potato, some features were selected as the main parameters to monitor the chnges in product during storage period: Major Axis Length, Compactness, and area (morphological features), L* and b* (color features) and Average contrast (Ac) and Average gray level (Agl) (texture features). Among the training algorithms, Levenberg–Marquardt (LM) training algorithm with the lowest root mean square error (RMSE=0.012) and the highest coefficient of determination (R2=95.01) were considered as an optimal model for classification of two samples stored in non-technical and technical storage. The accuracy of identification of the Agria genotype was 89.2% and 87.6%, and the accuracy of the genotype Clone 8-397009 was 92.4% and 90.3%, in non-technical and technical storage respectively.
Amir Hossein Afkari Sayyah; Hamid Reza Mohammad Doust Chaman Abad; Mansour Rasekh; Mahsa Sadat Razavi
Abstract
Machine vision technology can be used to detect the location of weeds around the main crop in a field as the machine moves, in order to decrease the losses of using herbicides. The purpose of this research is to determine the accuracy of image processing method in discriminating weeds from potato crop ...
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Machine vision technology can be used to detect the location of weeds around the main crop in a field as the machine moves, in order to decrease the losses of using herbicides. The purpose of this research is to determine the accuracy of image processing method in discriminating weeds from potato crop according to color features. Therefore, by performing a research which conducted in university of Mohaghegh Ardabili research field, three factors including: environmental light condition, type of weed (Shalambig, Pichack and Wheat), and level of crop growth, were investigated on discrimination accuracy. The results of this research were showed that there are no significant differences between two types of environmental light conditions. However, the main and interactive effects of two factors of type of weeds and level of crop growth was significant on discriminating system performance. According to this study, the first stage of crop growth is the best time for the visual tests (middle of the June) and among the three types of popular weed in the region, it is possible to discriminate the wheat from potato leaves with a reasonable accuracy according to RGB color model. By this method, it is possible to determine the location of weeds around the main crop by maximum accuracy of 95% dependent to different condition of treatments.
