Image Processing
A. Jahanbakhshi; K. Kheiralipour
Abstract
Introduction Carrot is one of the most important agricultural products used by millions of people all over the world. Quality assessment of agricultural products is one of the most important factors in improving the marketability of agricultural products. In the market, carrots with irregular shapes ...
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Introduction Carrot is one of the most important agricultural products used by millions of people all over the world. Quality assessment of agricultural products is one of the most important factors in improving the marketability of agricultural products. In the market, carrots with irregular shapes are not commonly picked by customers due to their appearance. This causes to remain those carrots in the market for a long time and then decay. Therefore, adopting an appropriate method for sorting and packaging this product can increase its desirability in the market. Packaging and sorting of carrots by workers bring about many problems such as high cost, product waste, etc. Image processing systems are modern methods which have different applications in agriculture including sorting of different products. The aim of this study was to implement a machine vision system to classify carrot based on their shape using image processing. Materials and Methods In this study, 135 carrot samples with different shapes (56 regulars and 79 irregulars) were selected and their images were obtained through an imaging system. First, an expert divided the carrots into, two categories according to their shapes. The carrots which had irregular shape were those with double or triple roots, cracked carrots, curved carrots, damaged carrots, and broken ones and those with upright shapes were considered as regular shape carrot. After imaging, image processing was started by an algorithm programmed in Matlab R2012a medium. Then some shape features such as length, width, breadth, perimeter, elongation, compactness, roundness, area, eccentricity, centroid, centroid non-homogeneity, and width non-homogeneity were extracted. After the selection of efficient features, artificial neural networks and support vector machine were used to classify the efficient features. Results and Discussion The number of neurons in the hidden layers of artificial neural network models were varied to find the optimal model. The highest percentage of the correct classification rate (98.50%) belonged to the structure of 2-10-16, which in fact has 16 neurons in the input layer, 10 neurons in the hidden layer and 2 neurons in the output layer. This model has also the lowest mean squared error and the highest correlation coefficient of the test data, 0.90 and 2.52, respectively. This network was a feed forward back propagation error type and the activation functions in hidden and output layers were Tansig and Perlin, respectively. The correct classification rate of the support vector machine method was 89.62%. The confusion matrix of support vector machine method showed that out of 56 usual samples, 42 specimens were correctly identified but 14 samples were mistakenly classified as unusual carrots. All 79 carrots with unusual shapes were correctly classified. The results obtained from the comparison of the performance of the two methods, the neural network method has a good superiority than the support vector machine for classification. Conclusion In this research, the classification of carrots was based on its appearance. At first the physical characteristics and appearance attributes of the carrot samples were extracted and processed using image processing. Image analysis was included the classification of samples into two usual and unusual shapes, which to classify the extracted properties two methods were used: the artificial neural network (ANN) and support vector machine (SVM). The classification accuracy of the ANN method was higher than SVM. It can be said that the image processing method can be used to improve the traditional method for grading the carrot product in new ways. So, the marketability of the product will be increased, and thus its losses will be reduced. Also, the image processing technique can be used as a simple, fast and non-destructive alternative to other methods of extracting geometric properties of agricultural products. Finally, it can be stated that image processing method and machine vision are effective ways for improving the traditional sorting techniques for carrots.
M. Naghipour Zade Mahani; M. H. Aghkhani
Abstract
Introduction: Carrot is one of the most common vegetables used for human nutrition because of its high vitamin and fiber contents. Drying improves the product shelf life without addition of any chemical preservative and reduces both the size of package and the transport cost. Drying also aidsto reduce ...
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Introduction: Carrot is one of the most common vegetables used for human nutrition because of its high vitamin and fiber contents. Drying improves the product shelf life without addition of any chemical preservative and reduces both the size of package and the transport cost. Drying also aidsto reduce postharvest losses of fruits and vegetables especially, which can be as high as 70%. Dried carrots are used in dehydrated soups and in the form of powder in pastries and sauces. The main aim of drying agricultural products is decrease the moisture content to a level which allows safe storage over an extended period. Many fruits and vegetables can be sliced before drying.because of different tissue of a fruit or vegetable, cutting them in different direction and shape created different tissue slices. Due to drying is the exiting process of the moisture from internal tissue so different tissue slices caused different drying kinetics. Therefore, the study on effect of cutting parameters on drying is necessary.
Materials and Methods: Carrots (Daucus carota L.) were purchased from the local market (Kerman, Iran) and stored in a refrigerator at 5°C. The initial moisture contents of the Carrot samples were determined by the oven drying method. The sample was dried in an oven at 105±2°C about 24 hours. The carrots cut by 3 models blade at 3 directions. The samples were dried in an oven at 70°C. Moisture content of the carrot slices were determined by weighting of samples during drying. Volume changes because of sample shrinkage were measured by a water displacement method. Rehydration experiment was performed by immersing a weighted amount of dried samples into hot water 50 °C for 30 min.
In this study the effect of some cutting parameters was considered on carrot drying and the quality of final drying product. The tests were performed as a completely random design. The effects of carrot thickness at two levels (3 and 6 mm), blade in 3 models (flat blade, wavy blade and Ridged blade) and the cutting direction at 3 levels (linear, lateral and diagonal) were evaluated on drying kinetics, drying rate, shrinkage and rehydration. Statistic analysis done by SPSS software.
Results and Discussion: The results of analysis of variance showed that the effects of cutting parameters were significant on studied parameters (p<0.01) (Table 1). Thin layers dried faster than thick layers because of firmness of surface which it causes slow moisture transfer. The least drying time was 200 minutes at the samples that cut by a wavy blade at the lateral direction with a significant difference (p<0.05) given Fig.3. In these samples surface evaporation is more, because of more surface. The compare means showed drying rate at thick layer is fewer because of the longer distance moisture removal (Fig.6). Also the most drying rate was 0.74 gmin-1 at cutting by flat blade on linear direction with a significant difference (p<0.05).The least shrinkage was obtained on this treatment was 36.7% given Fig.8. The most of tissue of linear slices is woody part that is dense compare with other parts therefore shrinkage decrease at during drying. The most rehydration was 3.96 and 3.88 for cutting by flat blade in diagonal and linear direction with significant difference to other treatments. Rehydration depends on cell damage greatly. Since the slices of carrot that cut by flat blade were damaged fewer than other treatments therefore rehydration was more.
Conclusions: The drying behavior of carrot slices was studied at different methods in slicing carrot. The results showed a significant effect of the cutting variables on drying kinetics, drying rate, shrinkage and rehydration. The carrot moisture content decreases continuously over the drying and the fastest drying occurred at thin layers sliced by wavy blade. The slices that were cut by flat blade at linear direction caused the best quality. The results show cutting parameters are significant effect on quality of dried fruits and vegetable. There for the study of drying behavior is necessary for fruits with different tissue because of more quality of production and high efficiency at drying. Also the study of cutting parameter suggest on other fruits and vegetables with different tissue. The results help to manufactures for improvement of production of drying equipment.
