Carrot Sorting Based on Shape using Image Processing, Artificial Neural Network, and Support Vector Machine

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.  Conclusions 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

Estimation of dust concentration by a novel machine vision system

The dust phenomenon is one of the main environmental problems that it reversely affects human health and economical and social activities. In the present research, a novel algorithm has been developed based on image processing to estimate dust concentration. An experimental setup was implemented to create airborne dust with different concentration values from 0 to 2750 mu g.m(-3). The images of the different dust concentration values were acquired and analyzed by image processing technique. Different color and texture features were extracted from various color spaces. The extracted features were used to develop single and multivariable models by regression method. Totally 285 single variable models were obtained and compared to select efficient features among them. The best single variable model had a predictive accuracy of 91. The features were used for multivariable modeling and the best model was selected with a predictive accuracy of 100 and a mean squared error of 1.44 x 10(-23). The results showed the high ability of the developed machine vision system for estimating dust concentration with high speed and accuracy

Influence of vermicompost and sheep manure on mechanical properties of tomato fruit

Mechanical properties of the horticultural products play an important role in improving the products quality and storage life after harvesting and also reducing product waste. Recently, using organic fertilizers has increasing trend for producing high‐quality products as well as improvement of soil quality. Two of the best options to produce organic material and sustainability of agricultural production are vermicompost and sheep manure. The present study relied on determination of mechanical properties through pressure and shear tests. Vermicompost and sheep manure were used separately to fertilize the soil. After planting tomato seeds and harvesting, tomato fruits were analyzed by a universal test machine. The results showed that vermicompost was a better fertilizer than sheep manure due to its more appropriate carbon to nitrogen ratio (C/N), acidity, and salinity. Also, in the pressure test, the maximum force required for bruise of tomato produced with vermicompost (41.5N) was more than that of control sample (no fertilizer) and sheep manure. In the shearing test, the maximum force required for shearing tomato produced with vermicompost (58.60 N) was lower than that of control sample (no fertilizer) and sheep manure. The findings of this study can be used to reduce the amount of waste at different stages of tomato production and supply including the design and optimization of processing and transportation equipment