A machine vision-intelligent modelling based technique for in-line bell pepper sorting

The uniformity of appearance attributes of bell peppers is significant for consumers and food industries.To automate the sorting process of bell peppers and improve the packaging quality of this crop by detecting and separating the not likable low-color bell peppers,developing an appropriate sorting system would be of high importance and influence.According to standards and export needs,the bell pepper should be graded based on maturity levels and size to five classes.This research has been aimed to develop a machine vision-based system equipped with an intelligent modelling approach for in-line sorting bell peppers into desirable and undesirable samples,with the ability to predict the maturity level and the size of the desirable bell peppers.Multilayer perceptron(MLP)artificial neural networks(ANNs)as the nonlinear modelswere designed for that purpose.TheMLP modelswere trained and evaluated through five-fold cross-validation method.The optimum MLP classifier was compared with a linear discriminant analysis(LDA)model.The results showed that the MLP outperforms the LDA model.The processing time to classify each captured image was estimated as 0.2 s/sample,which is fast enough for in-line application.Accordingly,the optimum MLP model was integrated with a machine vision-based sorting machine,and the developed system was evaluated in the in-line phase.The performance parameters,including accuracy,precision,sensitivity,and specificity,were 93.2%,86.4%,84%,and 95.7%,respectively.The total sorting rate of the bell pepper was also measured as approximately 3000 samples/h.

Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs

Energy is regarded as one of the most important elements in agricultural sector.During the last decades energy consumption in agriculture has increased,so finding the relationship between energy consumption and crop yields in agricultural production can help to achieve sustainable agriculture.In this study several adaptive neuro-fuzzy inference system(ANFIS)models were evaluated to predict wheat grain yield on the basis of energy inputs.Moreover,artificial neural networks(ANNs)were developed and the obtained results were compared with ANFIS models.For the best ANFIS structure gained in this study,R,RMSE and MAPE were calculated as 0.976,0.046 and 0.4,respectively.The developed ANN was a multilayer perceptron(MLP)with eleven neurons in the input layer,two hidden layers with 32 and 10 neurons and one neuron(wheat grain yield)in the output layer.For the best ANN model,R,RMSE and MAPE were computed as 0.92,0.9 and 0.1,respectively.The results illustrated that ANFIS model can predict the yield more precisely than ANN.

Design,development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine

In this study,an intelligent system based on combined machine vision(MV)and Support Vector Machine(SVM)was developed for sorting of peeled pistachio kernels and shells.The system was composed of conveyor belt,lighting box,camera,processing unit and sorting unit.A color CCD camera was used to capture images.The images were digitalized by a capture card and transferred to a personal computer for further analysis.Initially,images were converted from RGB color space to HSV color ones.For segmentation of the acquired images,H-component in the HSV color space and Otsu thresholding method were applied.A feature vector containing 30 color features was extracted from the captured images.A feature selection method based on sensitivity analysis was carried out to select superior features.The selected features were presented to SVM classifier.Various SVM models having a different kernel function were developed and tested.The SVM model having cubic polynomial kernel function and 38 support vectors achieved the best accuracy(99.17%)and then was selected to use in online decision-making unit of the system.By launching the online system,it was found that limiting factors of the system capacity were related to the hardware parts of the system(conveyor belt and pneumatic valves used in the sorting unit).The limiting factors led to a distance of 8 mm between the samples.The overall accuracy and capacity of the sorter were obtained 94.33% and 22.74 kg/h,respectively.

Fault diagnosis of tractor auxiliary gearbox using vibration analysis and random forest classifier

Accurate detection of mechanical components faults is an essential step for reduction of repair cost,human injury probability and loss of production.Using intelligent fault diagno-sis systems in tractor could prevent secondary damage,thereby avoiding heavy conse-quences.In this study,fault diagnosis of tractor auxiliary gearbox is presented.Vibration signals of healthy and faulty pinions gear under three different operational conditions(Rotational speeds of 600 RPM,1350 RPM and 2000 RPM)were collected,and discrete wave-let transform(DWT)was used as signal processing.Useful statistical features were calcu-lated from collected signals.Correlation-based feature selection(CFS)method was used to find the best features.Random forest(RF)and multilayer perceptron(MLP)neural net-works were employed to classify the data.The overall accuracy of RF classifier without using feature selection were 86.25%,at 600 RPM.The corresponding values of RF trained with the optimal 6 features by using CFS was 92.5%.The best results obtained at 1350 RPM,since the detection accuracy was 95%.The results of this study demonstrated the effectiveness and feasibility of the proposed method for fault diagnosis of tractor auxiliary gearbox.

Dielectric spectroscopy coupled with artificial neural network for classification and quantification of sesame oil adulteration

Adulteration using cheap vegetable oils into expensive oils such as sesame oil is a considerable challenge in the edible oil market. To discriminate pure and adulterated sesame oilwith sunflower and canola oils (commonly used as an adulterant to the high-price oils),dielectric spectroscopy was applied in the range of 40 kHz–20 MHz. The principal component analysis (PCA) plots were able to distinguish the pure sesame oil, while it was impossible to separate the adulterated oils based on the kind of adulteration. The correlationbased feature selection (CFS) method was used to select the more relevant dielectric datawithin the spectrum and to reduce the dimensionality of the input vector belongs to theartificial neural network (ANN). The ANN classifier with topology of 19-5-4 structureshowed a perfect accuracy of 100% in detecting the authentic and the adulterated sesameoil. The regression ANN with the topology of 15-5-1, 21-8-1 and 10-11-1 were the mostrobust models in quantifying the amount of adulteration in sesame oil generated by sun-flower oil, canola oil and sunflower + canola oils, with R2Test of 1, 1 and 0.999 9, respectively.The proposed technique is a powerful and simple method to detect and quantify adulteration of sesame oil. The novelty of this research is capability of used system for authentication of adulterated sesame oil using low frequency. Furthermore, the developed systemhas a good capability for other types of sesame oil adulterations as well as to detect adulteration in other expensive edible oils.