A. Moghimi; A. Sazgarnia; M. H. Aghkhani
Abstract
IntroductionPistachio production has been adversely affected by Psylla, which is a devastating insect. The primary goal of this study was to select sensitive spectral bands to distinguish pistachio leaves infected by Psylla from healthy leaves. Diagnosis of psylla disease before the onset of visual cues ...
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IntroductionPistachio production has been adversely affected by Psylla, which is a devastating insect. The primary goal of this study was to select sensitive spectral bands to distinguish pistachio leaves infected by Psylla from healthy leaves. Diagnosis of psylla disease before the onset of visual cues is crucial for making decisions about topical garden management. Since it is not possible to diagnose psylla disease even after the onset of symptoms with the help of color images by drones, hyperspectral and multispectral sensors are needed. The main purpose of this study was to extract spectral bands suitable for distinguishing healthy leaves from psylla leaves. For this purpose, in this paper, a new method for selecting sensitive spectral properties from hyperspectral data with the high spectral resolution is presented. The intelligent selection of sensitive bands is a convenient way to build multispectral sensors for a specific application (in this article, the diagnosis of psylla leaves). Knowledge of disease-sensitive wavelengths can also help researchers analyze multispectral and hyperspectral aerial images captured by satellites or drones.Materials and MethodsA total number of 160 healthy and diseased leaves were scanned in 64 spectral bands between 400-1100 nm with 10 nm spectral resolution. A random forest algorithm was used to identify the importance of features in classifying the dataset into diseased and healthy leaves. After computing the importance of the features, a clustering algorithm was developed to cluster the most important features into six clusters such that the center of clusters was 50 nm apart. To transfer the hyperspectral dataset into a multispectral dataset, the reflectance was averaged in spectral bands within ±15 nm of each cluster center and achieved six broad multispectral bands. Afterwards a support vector machine algorithm was utilized to classify the diseased and healthy leaves using both hyperspectral and multispectral datasets.Results and DiscussionThe center of clusters were 468 nm, 598 nm, 710 nm, 791 nm, 858 nm, and 1023 nm, which were calculated by taking the average of all the members assigned to the individual clusters. These are the most informative spectral bands to distinguish the pistachio leaves infected by Psylla from the healthy leaves. The F1-score was 90.91 when the hyperspectral dataset (all bands) was used, while the F1-score was 88.69 for the multispectral dataset. The subtle difference between the F1-scores indicates that the proposed pipeline in this study was able to select appropriately the sensitive bands while retaining all relevant information.ConclusionThe importance of spectral bands in the visible and near-infrared region (between 400 and 1100 nm) was obtained to identify pistachio tree leaves infected with psylla disease. Based on the importance of spectral properties and using a clustering algorithm, six wavelengths were obtained as the best wavelengths for classifying healthy and diseased pistachio leaves. Then, by averaging the wavelengths at a distance of 15 nm from these six centers, the hyperspectral data (64 bands) became multispectral (6 bands). Since the correlation between the wavelengths in the near-infrared region was very high (more than 95%), out of the three selected wavelengths in the near-infrared region (710, 791, and 1023), only the 710-nm wavelength, which was closer to the visible region, was selected. The results of classification of infected and diseased leaves using hyperspectral and multispectral data showed that the degree of classification accuracy decreases by about 2% and if only 4 bands are used, the degree of accuracy decreases by about 3%.The results of this study revealed that the proposed framework could be used for selecting the most informative spectral bands and accordingly develop custom-designed multispectral sensors for disease detection in pistachio. In addition, we could reduce the dimensionality of the hyperspectral datasets and avoid the issues related to the curse of dimensionalitylity.
M. H. Aghkhani; J. Baradaran Motie
Abstract
Introduction: Separation and grading of agricultural products from the production to supply, has notable importance. The separation can be done based on physical, electrical, magnetic, optical properties and etc. It is necessary for any development of new systems to study enough on the properties and ...
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Introduction: Separation and grading of agricultural products from the production to supply, has notable importance. The separation can be done based on physical, electrical, magnetic, optical properties and etc. It is necessary for any development of new systems to study enough on the properties and behavior of agricultural products.
Some characteristics for separation are size (length, width and thickness), hardness, shape, density, surface roughness, color, speed limit, aerodynamic properties, electrical conductivity, elasticity and coefficient of static friction point.
So far, the friction properties of agricultural products used in the separating process, but the effect of electrostatic charging on static and dynamic coefficients of friction for separation had little attention. The aim of this study was to find out the interactions between electrostatic and friction properties to find a way to separate products that separation is not possible with conventional methods or not sufficiently accurate. In this paper, the separation of close and smiley pistachios by electrostatic charging was investigated.
Materials and Methods: Kallehghoochi pistachio cultivar has the top rank in production in Iran. Therefore, it was used as a sample.
The experimental design that used in this study, had moisture content at three levels (24.2, 14.5 and 8.1 percent), electric field intensity at three levels (zero, 4000 and 7000 V), speed of movement on the surface at three levels (1300, 2500 and 3300 mm per minute), friction surface (galvanized sheet iron, aluminum and flat rubber) and pistachio type at two levels (filled splits and closed) that was measured and analyzed in completely randomized factorial design.
A friction measuring device (built in Ferdowsi University of Mashhad) used to measure the friction force. It has a removable table that can move in two directions with adjustable speed. The test sample put into the vessel with internal dimensions of 300 × 150 × 25 mm and with wall thickness of 5 mm placed on trolleys. In the bottom of the container a separate aluminum plate was installed as the negative pole of the electric field. The friction plates as a positive pole placed on top of the sample. There were no contact between friction plates and walls of vessel (samples were about 2 to 3 mm higher from the edges of wall).
Frictional force changes due to movement of table, measured and recorded by an accurate load cell. From force-displacement curves, the coefficient of dynamic friction and static coefficient of friction calculated. In general, according to the experimental design, 486 tests were performed.
Results and Discussion: According to the results of statistical analysis, there is significant interaction affect between pistachios type and electrical field, as well as, the interaction between electrical field and speed, on dynamic coefficient of friction. It means two pistachio types can be separated by electrical charging.
Different physical properties of surface of filled non-splits pistachio nuts (such as corners and edges) and filled splits ones, caused differences in the distribution of electric charge and as a result, its interaction with the electric field were significant.
Changes in dynamic coefficient of friction according to the electric field intensity at different levels of moisture content and speed on the friction surfaces of iron, aluminum and rubber, was drawn in Fig.4, 5 and 6, respectively. These figures reflected the reduction of dynamic coefficient of friction by increasing the movement speed of table.
According to Fig.7, increasing the intensity of the electric field increases the dynamic coefficient of friction. Because this leads to build the opposition charge on samples and galvanized iron sheets, and with increase of electrical field, these charges will rise.
Fig.9 shows different trends of variation of dynamic coefficient of friction against moisture on rubber surface. This chart shows the higher coefficient of friction of filled non-splits samples than filled splits in all cases and shows an increasing trend with increasing humidity.
Conclusions: Table 2 presents the dynamic coefficients of friction in different states on different levels of moisture content. According to this table, the maximum difference was achieved in moisture content of 8% (which is close to the product storage moisture) in rubber surface with field strength of 7000 V and 1300 mm per minute speed. On 14 percent moisture content, the maximum difference was achieved on aluminum surface by 2500 millimeter per minute speed and 7000 V field strength. By the results, on 24 percent moisture content (the moisture close to peeling process) the maximum difference between filled non-splits and filled splits pistachios friction was achieved on aluminum surface, 7000 V electric field strength and 2500 millimeter per minute table speed.
Thus, to have a separation system, the aluminum surface, 7000 V electric field strength and adjustable speed between 1300 to 2500 mm per minute is recommended.
T. Mohammadi Moghaddam; S. M. A. Razavi; M. Taghizadeh; A. Sazgarnia; B. Pardhan
Abstract
Introduction: Pistachio nut is one of the most delicious and nutritious nuts in the world and it is being used as a salted and roasted product or as an ingredient in snacks, ice cream, desserts, etc. (Maghsudi, 2010; Kashaninejad et al. 2006). Roasting is one of the most important food processes which ...
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Introduction: Pistachio nut is one of the most delicious and nutritious nuts in the world and it is being used as a salted and roasted product or as an ingredient in snacks, ice cream, desserts, etc. (Maghsudi, 2010; Kashaninejad et al. 2006). Roasting is one of the most important food processes which provides useful attributes to the product. One of the objectives of nut roasting is to alter and significantly enhance the flavor, texture, color and appearance of the product (Ozdemir, 2001). In recent years, spectral imaging techniques (i.e. hyperspectral and multispectral imaging) have emerged as powerful tools for safequality inspection of various agricultural commodities (Gowen et al., 2007). The objectives of this study were to apply reflectance hyperspectral imaging for non-destructive determination of moisture content and hardness of pistachio kernels roasted in different conditions.
Materials and methods: Dried O’hadi pistachio nuts were supplied from a local market in Mashhad. Pistachio nuts were soaked in 5L of 20% salt solution for 20min (Goktas Seyhan, 2003). For roasting process, three temperatures (90, 120 and 150°C), three times (20, 35 and 50 min) and three air velocities (0.5, 1.5 and 2.5 m s-1) were applied. The moisture content of pistachio kernels was measured in triplicate using oven drying (3 gr samples at 105 °C for 12 hours). Uniaxial compression test by a 35mm diameter plastic cylinder, was made on the pistachio kernels, which were mounted on a platform. Samples were compressed at a depth of 2mm and speed of 30 mm min-1. A hyperspectral imaging system in the Vis-NIR range (400-1000 nm) was employed. The spectral pre-processing techniques: first derivative and second derivative, median filter, Savitzkye-Golay, wavelet, multiplicative scatter correction (MSC) and standard normal variate transformation (SNV) were used. To make models at PLSR and ANN methods, ParLeS software and Matlab R2009a were used, respectively. The coefficient of determination (R2), the root mean square error of prediction (RMSEP) and the ratio of the standard deviation of the response variable to RMSEP (known as relative performance determinant (RPD)) were calculated.
Results and discussion:
Interpretation of hyperspectral data: The results showed that the spectra of the shell, the whole kernel and the internal part of the kernel have different patterns. The internal part of thekernel had 2 peaks at 630 nm and 690 nm, while the shell and the whole kernel had 1 peak at 670 nm and 720 nm, respectively and the peak of the whole kernel was sharper than that of the shell. The highest and lowest intensities were for the internal part of the kernel and the whole kernel, respectively. The spectral slope of the internal part is higher than that of the shell and the whole kernel at 500-700 nm.
The effect of different pre-processing techniques and analysis on prediction of pistachio kernels properties: In the absence of pre-processing techniques, low correlation coefficients were observed for prediction of moisture content and hardness. However, with the use of pre-processing techniques, in some models, correlation coefficient and RPD increased and the RMSEP decreased. The results revealed that ANN models would predict moisture content and textural characteristics of roasted pistachio kernels better than PLSR models.
Moisture content: ANN models can predict moisture content of roasted pistachio kernels better than PLSR models. In total, PLSR models showed low RPD and R2. For all samples, RPD was lower than 1.5, indicating that the developed models do not give an accurate prediction for moisture content. The best results with ANN method were achieved using a combination of SNV, wavelet and D1 for predicting moisture content with R2 =0.907 and RMSEP=0.179.
Hardness: The results indicated that ANN models can predict the hardness better than PLSR models. The best results with PLSR models were achieved using a combination of SNV, wavelet and D1 with R2= 0.643, RMSEP=10.78, RDP= 1.48 and 2 PLSR factors. However, due to high RMSEP and low R2 and RPD, it can be mentioned that prediction of hardness values with ANN model was not sufficiently desirable. However it was better than the PLSR models. The best results with ANN models were achieved using a combination of SNV, wavelet and D2 with R2=0.876 and RMSEP=5.216.
Conclusions: The results of this study showed that employing pre-processing methods causesa decrease in prediction error and improves the quality of the models. ANN models could predict moisture content and hardness of roasted pistachio kernels better than the PLSR models.
R. Mohammadigol; M. H. Khoshtaghaza; R. Malekfar; M. Mirabolfathi; A. M. Nikbakht
Abstract
Pistachio contamination to aflatoxin has been known as a serious problem for pistachio exportation. With regards to the increasing demand for Raman spectroscopy to detect and classify different materials and also the current experimental and technical problems for measuring toxin (such as being expensive ...
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Pistachio contamination to aflatoxin has been known as a serious problem for pistachio exportation. With regards to the increasing demand for Raman spectroscopy to detect and classify different materials and also the current experimental and technical problems for measuring toxin (such as being expensive and time-consuming), the main objective of this study was to detect aflatoxin contamination in pistachio by using Raman spectroscopy technique and artificial neural networks. Three sets of samples were prepared: non-contaminated (healthy) and contaminated samples with 20 and 100 ppb of the total aflatoxins (B1+B2+G1+G2). After spectral acquisition, considering to the results, spectral data were normalized and then principal components (PCs) were extracted to reduce the data dimensions. For classification of the samples spectra, an artificial neural network was used with a feed forward back propagation algorithm for 4 inputs and 3 neurons in hidden layer. Mean overall accuracy was achieved to be 98 percent; therefore, non-liner Raman spectra data modeling by ANN for samples classification was successful.
A. R. Salari Kia; M. H. Aghkhani; M. H. Abbaspour-Fard
Abstract
Pistachio has a special ranking among Iranian agricultural products. Iran is known as the largest producer and exporter of pistachio in the world. Agricultural products are imposed under different thermal treatments during storage and processing. Designing all these processes requires thermal parameters ...
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Pistachio has a special ranking among Iranian agricultural products. Iran is known as the largest producer and exporter of pistachio in the world. Agricultural products are imposed under different thermal treatments during storage and processing. Designing all these processes requires thermal parameters of the products such as specific heat capacity. Regarding the importance of pistachio processing as an exportable product, in this study the specific heat capacity of nut and kernel of two varieties of Iranian pistachio (Kalle-Ghochi and Badami) were investigated at four levels of moisture content (initial moisture content (5%), 15%, 25% and 40% w.b.) and three levels of temperature (40, 50 and 60°C). In both varieties, the differences between the data were significant at the 1% of probability; however, the effect of moisture content was greater than that of temperature. The results indicated that the specific heat capacity of both nuts and kernels increase logarithmically with increase of moisture content and also increase linearly with increase of temperature. This parameter has altered for nut and kernel of Kalle-Ghochi and Badami varieties within the range of 1.039-2.936 kJ kg-1 K-1, 1.236-3.320 kJ kg-1 K-1, 0.887-2.773 kJ kg-1 K-1 and 0.811-2.914 kJ kg-1 K-1, respectively. Moreover, for any given level of temperature, the specific heat capacity of kernels was higher than that of nuts. Finally, regression models with high R2 values were developed to predict the specific heat capacity of pistachio varieties as a function of moisture content and temperature
