Intelligent detection of citrus fruit pests using machine vision system and convolutional neural network through transfer learning technique

Plant pests and diseases play a significant role in reducing the quality of agricultural products. As one of the most important plant pathogens, pests like Mediterranean fruit fly cause significant damage to crops and thus annually farmers face a lot of loss in their products. Therefore, the use of modern and non-destructive methods such as machine vision systems and deep learning for early detection of pests in agricultural products is of particular importance. In this study, citrus fruit images were taken in three stages: 1) before pest infestation, 2) beginning of fruit infestation, and 3) eight days after the second stage, in natural light conditions (7000-11,000 lux). A total of 1519 images were prepared for all classes. To classify the images, 70% of the images were used for the network training stage, 10% and 20% of the images were used for the validation and testing stages. Four pre-trained CNN models, namely ResNet-50, GoogleNet, VGG-16 and AlexNet as well as the SGDm, RMSProp and Adam optimization algorithms were used to identify and classify healthy fruit and fruit infected with the Mediterranean fly. The results of evaluating the models in the pest outbreak stage showed that the VGG-16 model with the help of SGDm algorithm had the best efficiency with the highest detection accuracy and F1 of 98.33% and 98.36%, respectively. The evaluation of the third stage showed that the AlexNet model with the help of SGDm algorithm had the best result with the highest detection accuracy and F1 of 99.33% and 99.34%, respectively. AlexNet model using SGDm optimization algorithm had the shortest network training time (323 s). The results of this study showed that convolutional neural network method and machine vision system can be effective in controlling and managing pests in orchards and other agricultural products.

Detection of fraud in ginger powder using an automatic sorting system based on image processing technique and deep learning

Ginger is a well-known product in the food and pharmaceutical industries. Ginger is one of the spices which are adulterated for economic gain. The lack of marketability of grade 3 chickpeas (small and broken chickpeas) and their very low price have made them a good choice to be mixed with ginger in powder form and sold in the market. Demand for non-destructive methods of measuring food quality, such as machine vision and the growing need for food and spices, were the main motives to conduct this study. This study classified ginger powder images to detect fraud by improving convolutional neural networks (CNN) through a gated pooling function. The main approach to improving CNN is to use a pooling function that combines average pooling and max pooling. The Batch normalization (BN) technique is used in CNN to improve classification results. We show empirically that the combining operation used increases the accuracy of ginger powder classification compared to the baseline pooling method. For this purpose, 3360 image samples of ginger powder were prepared in 7 categories (pure ginger powder, chickpea powder, 10%, 20%, 30%, 40%, and 50% fraud in ginger powder). Moreover, MLP, Fuzzy, SVM, GBT, and EDT algorithms were used to compare the proposed CNN results with other classifiers. The results showed that using batch normalization based on gated pooling, the proposed CNN was able to grade the images of ginger powder with 99.70% accuracy compared to other classifiers. Therefore, it can be said that the CNN method and image processing technique effectively increase marketability, prevent ginger powder fraud, and promote traditional methods of ginger powder fraud detection.

A novel method based on machine vision system and deep learning to detect fraud in turmeric powder

Assessing the quality of food and spices is particularly important in ensuring proper human nutrition. The use of computer vision method as a non-destructive technique in measuring the quality of food and spices has always been taken into consideration by researchers. Due to the high nutritional value of turmeric among the spices as well as the fraudulent motives to gain economic profit from the selling of this product, its quality assessment is very important. The lack of marketability of grade 3 chickpeas (small and broken chickpeas) and their very low price have made them a good choice to be mixed with turmeric in powder form and sold in the market. In this study, an improved convolutional neural network (CNN) was used to classify turmeric powder images to detect fraud. CNN was improved through the use of gated pooling functions. We also show with a combined approach based on the integration of average pooling and max pooling that the accuracy and performance of the proposed CNN has increased. In this study, 6240 image samples were prepared in 13 categories (pure turmeric powder, chickpea powder, chickpea powder mixed with food coloring, 10, 20, 30, 40 and 50% fraud in turmeric). In the preprocessing step, unwanted parts of the image were removed. The data augmentation (DA) was used to reduce the overfitting problem on CNN. Also in this research, MLP, Fuzzy, SVM, GBT and EDT algorithms were used to compare the proposed CNN results with other classifiers. The results showed that prevention of the overfitting problem using gated pooling, the proposed CNN was able to grade the images of turmeric powder with 99.36% accuracy compared to other classifiers. The results of this study also showed that computer vision, especially when used with deep learning (DL), can be a valuable method in evaluating the quality and detecting fraud in turmeric powder.

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images

Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these networks. In this paper, we introduce a strategy for data augmentation using the determination of the type and value of noise density to improve the robustness and generalization of deep CNNs for COVID-19 detection. Firstly, we present a learning-to-augment approach that generates new noisy variants of the original image data with optimized noise density. We apply a Bayesian optimization technique to control and choose the optimal noise type and its parameters. Secondly, we propose a novel data augmentation strategy, based on denoised X-ray images, that uses the distance between denoised and original pixels to generate new data. We develop an autoencoder model to create new data using denoised images corrupted by the Gaussian and impulse noise. A database of chest X-ray images, containing COVID-19 positive, healthy, and non-COVID pneumonia cases, is used to fine-tune the pre-trained networks (AlexNet, ShuffleNet, ResNet18, and GoogleNet). The proposed method performs better results compared to the state-of-the-art learning to augment strategies in terms of sensitivity (0.808), specificity (0.915), and F-Measure (0.737). The source code of the proposed method is available at https://github.com/mohamadmomeny/Learning-to-augment-strategy.

Novel environmentally friendly fuel: The effect of adding graphene quantum dot (GQD) nanoparticles with ethanol-biodiesel blends on the performance and emission characteristics of a diesel engine

Biodiesel fuel has some disadvantages including increase in NO_x, poor atomization and incomplete combustion. Additives and catalysts can be used to reduce the negative effects of biodiesel fuel. In addition, the use of metal oxide and metal nanoparticles causes environmental hazards. However, using biodegradable nanoparticles can significantly reduce such concerns. The present study investigated the effect of adding GQD + E to B10 fuel on the emission and performance characteristics of a diesel engine. B10 was blended with GQD (90 ppm) and bioethanol (E2, E4, E6 and E8% vol). Performance and emission characteristics, including power, torque, SFC, CO, CO₂, UHC and NO_x emissions were measured at the speeds of 1800, 2100 and 2400 rpm and full load mode. According to the results, the addition of GQD + E to B10 improved torque and power and decreased SFC, CO, UHC and NO_x. Finally, the B10 + E6 + GQD90 fuel was the best fuel regarding improved engine performance and reduced exhaust emission. The average of changes in power and torque, SFC, CO, UHC and NO_x compared to D100 for B10 + E6 + GQD90 were + 15.69%, +15.39%, -17.58%, -30.30%, -38.91% and -1.54%, respectively.

Evaluation of image processing technique and discriminant analysis methods in postharvest processing of carrot fruit

The most important process before packaging and preserving agricultural products is sorting operation. Sort of carrot by human labor is involved in many problems such as high cost and product waste. Image processing is a modern method, which has different applications in agriculture including classification and sorting. The aim of this study was to classify carrot based on shape using image processing technique. For this, 135 samples with different regular and irregular shapes were selected. After image acquisition and preprocessing, some features such as length, width, breadth, perimeter, elongation, compactness, roundness, area, eccentricity, centroid, centroid nonhomogeneity, and width nonhomogeneity were extracted. After feature selection, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were used to classify the features. The classification accuracies of the methods were 92.59 and 96.30, respectively. It can be stated that image processing is an effective way in improving the traditional carrot sorting techniques.

Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS

This study aimed to predict the drying kinetics, energy utilization (Eu ), energy utilization ratio (EUR), exergy loss, and exergy efficiency of quince slice in a hot air (HA) dryer using artificial neural networks and ANFIS. The experiments were performed at air temperatures of 50, 60, and 70°C and air velocities of 0.6, 1.2, and 1.8 m/s. The thermal parameters were determined using thermodynamic relations. Increasing air temperature and air velocity increased the effective moisture diffusivity (Deff ), Eu , EUR, exergy efficiency, and exergy loss. The value of the Deff was varied from 4.19 × 10-10 to 1.18 × 10-9 m2/s. The highest value Eu , EUR, and exergy loss and exergy efficiency were calculated 0.0694 kJ/s, 0.882, 0.044 kJ/s, and 0.879, respectively. Midilli et al. model, ANNs, and ANFIS model, with a determination coefficient (R 2) of .9992, .9993, and .9997, provided the best performance for predicting the moisture ratio of quince fruit. Also, the ANFIS model, in comparison with the artificial neural networks model, was better able to predict Eu , EUR, exergy efficiency, and exergy loss, with R 2 of .9989, .9988, .9986, and .9978, respectively.

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.

Evaluation of engineering properties for waste control of tomato during harvesting and postharvesting

In Iran, more than 30% of agricultural products turn into waste at different stages from harvesting to consumption. Thus, main factors for performing of this present study are including of: (a) the importance of tomato as an agricultural product and (b) lack of information about reducing waste during tomato processing. In this study, some physical, nutritional, mechanical, and hydrodynamic properties of tomato were measured under standard conditions. Physical properties included the length, width, thickness, mean diameter (geometric and arithmetic), mass, volume, density, sphericity, surface area, and aspect ratio. Also, nutritional properties, moisture, dry matter, pH, total soluble solid (TSS), and titration acidity (TA) of tomato were evaluated. The mechanical properties of tomato (compression and shear) were measured using Instron instrument. The hydrodynamic properties were measured with water in transportation, separation, and sorting of tomatoes. The physical properties were including of length, width, thickness, mass, volume, and geometric and arithmetic mean diameters showed a direct relationship with the size of tomatoes. Also, volumetric mass (density) had an inverse relation with tomato size. Yield point and shear force were obtained 51.27 and 22.20 N, respectively. The nutritional properties such as pH value, TSS, and TA were equal to 4.22, 22.23οBrix, and 2%, respectively. The hydrodynamic properties of tomatoes such as the terminal velocity, the tomatoes' rise time in the water column, the buoyancy force, and the drag force were obtained to be equal to 0.05 m/s, 10.11 S, 0.52 N, and 0.17 N, respectively.

Determination of physical and mechanical properties of carrot in order to reduce waste during harvesting and post-harvesting

Lack of sufficient knowledge about the physical and mechanical properties of agricultural products can result in higher waste of them. Due to the importance of carrot as an agricultural product and lack of much knowledge about how to reduce its waste as well as design and optimize the required harvest and postharvest machinery, this research study was carried out to fill this gap. In this study, physical properties included the length, width, thickness, mean diameter (geometric and arithmetic), mass, volume, density, sphericity, surface area, aspect ratio. The mechanical properties of the samples and their lengths were measured under the conditions of pressure (bruise), bending (break), and shearing of the carrot halves using a Zwick/Roell Instron testing machine based on the recommended standards. The mean geometric mean diameter, surface area, sphericity, volume and true density of the carrot were 49.54 mm, 7758.32 mm2, 0.32%, 70 cm3, and 1.04 g/cm3. In the study of mechanical properties of carrots, the maximum forces required for bruising, bending, and shearing of the carrot fruit were 71.90, 48.60, and 41.14 N, respectively. The results obtained about the physical and mechanical properties can be very useful in reducing carrot waste and mechanizing harvest and postharvest operations by providing us with information that helps us design machinery needed to transfer, sort, separate, wash, package, store, and process carrots.

Comparing Intelligence Quotient (IQ)among 3 to 7-year-old strabismic and nonstrabismic children in an Iranian population

This study was designed to compare the Intelligence Quotient (IQ) among 3 to 7-year-old strabismic and nonstrabismic children in an Iranian population.

In this cross-sectional study, 108 preschool children with equal numbers of strabismic/non-strabismic disorder (age 3–7 years) were randomly selected from exceptional strabismus clinics of Ahvaz and were evaluated with the preschool and primary scale of intelligence versions of Wechsler (WPPSI).

In the current study, 108 children were evaluated. In strabismic patients the mean performance, verbal and total IQ were 89.46±19.79, 89.57±21.57 and 91.54±22.08 respectively. These mean scores in normal children were 91.89±47.53, 87.56±15.6 and 89.96±17.62consecuently. The results showed that these three different IQ subscales were not significantly different among 3 to 7 years old strabismic and nonstrabismic children ((P>0.05 for all comparisons). There was no significant difference in IQ between two sexes (P>0.05) while Persian tribe children had greater IQ score compared to other tribes (P<0.05). Also, higher paternal educational status of children related to higher IQ score. IQ score was better in combined deviations and was higher in exotropes than esotropes; however, these differences were not statistically significant (P>0.05).

In this evaluation, we did not found a significant negative interference of strabismus on IQ score of preschool children. It can be concluded that paternal educational level and tribe have a significant effect on intelligent quotient, while this is not the case on sex and ocular deviation.

Effect of subacute exposure to silver nanoparticle on some hematological and plasma biochemical indices in silver carp (Hypophthalmichthys molitrix)

The use of silver nanoparticles (Ag-NPs) is rapidly increasing, but there are limited data on their effects on the aquatic environment. The present study aimed to determine the acute toxicity and evaluate the effect of subacute concentrations of Ag-NPs (Nanocid®: average particle size of 61 nm) on hematological and plasma biochemical indices of silver carp, Hypophthalmichthys molitrix, after 3, 7 and 14 days. The 24-, 48-, 72- and 96-h median lethal concentration (LC50) values of Nanocid for silver carp were estimated at 0.810, 0.648, 0.383 and 0.202 mg/L, respectively; 20% and 10% of the 96-h LC50 values (0.04 and 0.02 mg/L) were selected for subacute study. Red blood cell (RBC) count, hemoglobin (Hb) count and hematocrit (Hct) level were significantly reduced at both concentrations tested ( p < 0.05). White blood cell (WBC), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), cortisol and glucose levels in Nanocid-treated groups were significantly higher than the controlled group at experimental periods ( p < 0.05). In conclusion, Ag-NPs intoxication resulted in erythrocyte reduction, hematological disturbances, leucocytosis and stress response in silver carp and offered a simple tool to evaluate toxicity-derived alterations.

Normal interpupillary distance values in an Iranian population

PURPOSE

To report normal interpupillary distance (IPD) values in different age groups of an Iranian population.

METHODS

This study was performed on 1,500 randomly selected subjects from 3,260 consecutive out-patients with refractive errors referred to Farabi Eye Hospital, Isfahan, Iran over a period of two years (2008 to 2010). Measurement of refractive errors and IPD for far distance were performed using an autorefractometer (RMA-3000 autorefractometer, Topcon, Tokyo, Japan).

RESULTS

Mean IPD in adult subjects was 61.1±3.5 mm in women and 63.6±3.9 mm in men (p<0.001). Mean IPD increased 4.8 mm during the second decade, 1.7 mm during the third decade, and 0.6 mm during the fourth and fifth decades of life.

CONCLUSION

The observed increase in IPD after the age of 30 years indicates that factors other than skeletal growth may affect this parameter.