Aerodynamic separation and cleaning of pomegranate arils from rind and white segments (locular septa)

In the process of pomegranate arils pneumatic separation, the aerodynamic characteristics of pomegranate aril, rind and locular septa are essential. The main aim of this study was to measure and compare the aerodynamic characteristics of these materials to provide the data and to facilitate the design and adjustment of machines that perform separation of pomegranate arils from rind and locular septa based on aerodynamic characteristics (terminal velocity, drag coefficient and Reynolds number). To achieve this objective, Ashraf variety pomegranate fruit during its maturity stages was studied. The obtained results showed that the variation in maturity stage significantly influenced the terminal velocity, drag coefficient and Reynolds number (P < 0.05). During the fruit maturity, the terminal velocity of locular septa, rind and pomegranate aril increased from 1.05 to 1.16, 3.16 to 3.73 and 5.89 to 6.70 m s−1, respectively. The corresponding value of drag coefficient of the three studied materials decreased from 0.92 to 0.79, 0.75 to 0.59 and 0.53 to 0.36, respectively with advancing fruit maturity. Also these ranges for Reynolds number were 403.24–617.75, 1213.44–1986.37 and 2261.76–3568.02, respectively. Consequently, aerodynamic separation of pomegranate aril from locular septa and rind is theoretically possible if the air velocity value is adjusted according to the terminal velocity of pomegranate aril. Also the obtained equations can be used for calculating the parameters of pomegranate aril movement in pneumatic tunnels or in the design and development of air conveyor and pneumatic separator of pomegranate aril

Determining quality and maturity of pomegranates using multispectral imaging

In this paper, we investigated the use of multispectral imaging technique to quantify pomegranate fruit quality. Three quality factors including total soluble solids (TSS), pH and firmness were studied at four different maturity stages of 88, 109, 124 and 143 days after full bloom (DAFB) and were correlated with the spectral information extracted from images taken at four wavelength spectra. TSS, pH and firmness of the same samples were recorded using nondestructive methods and then modeled with their corresponding spectral data using partial least squire regression (PLSR). The correlation coefficient (r), RMSEC and RPD for the calibration models was found to be: r = 0.97, RMSEC = 0.21 °Brix and RPD = 6.7 °Brix for TSS; r = 0.93, RMSEC = 0.035 and RPD = 5.01 for pH; r = 0.95, RMSEC = 0.65 N and RPD = 5.65 N for firmness. Also these parameters for the validation models were as follows: r = 0.97, RMSEP = 0.22 °Brix and RPD = 5.77 °Brix for TSS; r = 0.94, RMSEP = 0.038 and RPD = 4.98 for pH; r = 0.94, RMSEP = 0.68 N and RPD = 5.33 N for firmness. The results demonstrated the capability of multispectral imaging and chemometrics as useful techniques to nondestructively monitoring pomegranate main quality attributes

Finite element analysis of the dynamic behavior of pear under impact loading

Pear fruit is susceptible to bruising from mechanical impact during field harvesting operations and at all stages of postharvest handling. The postharvest shelf life of bruised fruits were shorter, and they softened rapidly under cold storage compared with non-bruised samples. Developing strategies for reducing bruising during the supply chain requires an understanding of fruit dynamic behavior to different enforced loadings. Finite Element Method (FEM) is among the best techniques, in terms of accuracy and cost-efficiency, for studying the factors effective in impact-induced bruising. In this research, the drop test of pear sample was simulated using FEM. The simulation was conducted on a 3D solid model of the pear that was created by using non-contact optical scanning technology. This computer-based study aimed to assess the stress and strain distribution patterns within pear generated by collision of the fruit with a flat surface made of different materials. The contact force between two colliding surfaces is also investigated. The simulations were conducted at two different drop orientations and four different impact surfaces. Results showed that, in both drop orientations, the largest and smallest stresses, strains and contact forces were developed in collision with the steel and rubber surfaces, respectively. In general, these parameters were smaller when fruit collided with the surfaces along its horizontal axis than when collided along its vertical axis. Finally, analyses of stress and strain magnitudes showed that simulation stress and strain values were compatible with experiments data

Study on the performance of solar cells cooled with heatsink and nanofluid added with aluminum nanoparticle

The cooling of photovoltaic (PV) panels based on nanofluids is one of the emerging cooling methods to improve the efficiency of PV panels. In this study, the effects of aluminum nanoparticles on the cooling performance and conversion efficiency of PV panels were investigated experimentally. The surface temperature, output power, and efficiency of the PV panels were assessed in Mashhad, Iran on a sunny winter day in November 2020 under ambient temperatures between 10 and 17 °C. Experimental results indicated that the nanofluid with aluminum nanoparticle improved the solar panel efficiency and solar PV panel's output power by an average of 13.5 and 13.7%, respectively compared to that of water cooling without aluminum nanoparticles. A temperature reduction between 13.08 and 16.34 °C on the solar PV panels surface was observed for heatsink cooling with nanofluid containing aluminum nanoparticles. Overall, the results suggest that nanofluid added with aluminum nanoparticles is effective in improving the conversion efficiency of PV panels

Detection of foreign materials in cocoa beans by hyperspectral imaging technology

The presence of foreign materials in a batch of cocoa beans affect its profitability, marketability and overall quality grade of the product. Therefore, the identification of these materials and their subsequent removal is very important to ensure the high quality of the final product. This study aims to investigate the feasibility of using hyperspectral imaging technology for the detection and discrimination of four categories of foreign materials (wood, plastic, stone and plant organs) that are relevant to the cocoa processing industries. The spectral image data of 250 cocoa beans and foreign material was analyzed using principal component analysis and three classification models Support Vector Machine (SVM) Linear Discriminant Analyses (LDA) and K Nearest Neighbours (KNN). Optimal wavebands, which were obtained from the second spectra graph and the first three PCs, were fed into the classification models and the performance of classifiers was compared. The results showed that SVM could reach over 89.10% accuracy in classifying cocoa beans and foreign materials. The accuracy of the SVM classifier when using optimal features as input was 86.90% for the training set and 81.28% for the test set. An external test set of data was used to test the generalization of the model. The results showed that the classification of foreign materials could be more robust when the optimal feature was used as input data

Designing and Evaluating an Ultrasonic System for Identification of Weed Species

Introduction: Considering the importance of healthy and inexpensive agricultural production, it is necessary to seek ways for precisely discrimination of weeds in the field to minimize the use of herbicides. In this research the feasibility of weed detection due to the reflected ultrasonic waves from some common weeds including Portulacaceae, Chenopodium album L, Tribulus terrestris L, Amaranthus retroflexus L and Salsola iberica, was investigated. Materials and Methods: An electronic circuit with several parts such as a microcontroller, a power supply (5 DC volts), a RS-232 output port, and an ultrasonic wave generator and detector was constructed. It emits a 40 KHz ultrasonic wave and receives the recursive wave which is reflected from the weed canopy. It can be mounted on an adjustable tripod that is aligned along the three main directions (X, Y, and Z) and can also be turned around the X axis. The data acquisition was accomplished in the research field of the College of Agriculture, Ferdowsi University of Mashhad. The experiments were performed by mounting the system at constant height of 4 cm from the crop canopy. To avoid interfering of the recursive wave with the emitted wave, the generator and the detector were placed far apart. For each experiment the temperature and the relative humidity were recorded in a check list. For the Neural Network the so called BDLRF algorithm was used for training the network and started with a relatively constant large step size of learning rate and momentum term . Before destabilizing the network or when the convergence is slowed down, these values are decreased monotonically (22). In this study Double Sequential Classification Method was used for weed discrimination. This classification method can better simulate the human procedure for classification of different objects, from each other. The human being at the first stage, and based on some distinguishable criteria classifies the things into some main groups and then sorts each group to some other distinguishable subgroups and this procedure will continue up to all things to be classified from each other. Therefore, if a feature can separate more class from others, it is selected as optimum feature. But the optimum feature can only separate the limited numbers of groups in each stage. Other groups are separated with other optimum feature in some sequence stages. In this study the Double Sequential Classification Method is employed for the calcification of the weed species. Results and Discussion: Results showed that due to different surface and morphological characteristics of the crop canopy of the weed species under study, the pattern and the amplitude of the reflected ultrasonic waves are significantly different. The comparison of means of statistical features extracted from the reflected ultrasonic waves confirmed these differences. A Multi-Layers Perceptron (MLP) neural network, which was trained with a reduction learning rate, was developed and evaluated. The simultaneous separation of the five weed species showed that the error of detection during the training phase of Chenopodium album L was the highest among other species and was 16.67 percent, while the system was able to detect other species completely. However, the detection error for all species in the evaluation phase was more than 50 percent. Hence, a double sequential classification method was used through four sequential stages. In this method 11 neural networks were designed and finally four neural networks were selected. Results showed that Tribulus terrestris L was identified and separated completely from other species in the first stage, subsequently, Amaranthus retroflexus L in the second stage and Portulacaceae in the third stage, respectively. The remaining two species including, Chenopodium album L and Salsola iberica were successfully discriminated in the fourth stage. Conclusion: Results showed that this method can be a promising technique for real time identification and discrimination of different weed species in the field. It can be replaced with the conventional, laborious and expensive methods to reduce the final costs of agricultural production. Besides, it can reduce the consumption of herbicides in the fields. However, some efforts are required to implement the technique on the existing herbicide applicators or as a new machine for precision agriculture