Image Processing
M. H. Nargesi; K. Kheiralipour; F. Valizadeh Kakhki; Z. Moradi
Abstract
IntroductionPaying attention to the technical aspects of production plays a crucial role in increasing yield and ensuring sustainable agriculture. Organic fertilizers, such as poultry manure, contribute to plant growth by providing essential nutrients and improving soil quality. However, they alone cannot ...
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IntroductionPaying attention to the technical aspects of production plays a crucial role in increasing yield and ensuring sustainable agriculture. Organic fertilizers, such as poultry manure, contribute to plant growth by providing essential nutrients and improving soil quality. However, they alone cannot fully meet the nutritional needs of plants. The combination of organic and chemical fertilizers is an effective approach to enhancing soil fertility and boosting crop performance, ultimately leading to sustainable agricultural development. Integrated nutrient management also helps reduce the use of chemical fertilizers while minimizing their harmful effects on the environment. Potassium is an essential element in plant nutrition, playing a key role in processes such as photosynthesis, growth, chlorophyll production, and transpiration regulation. Additionally, under stress conditions, potassium enhances water uptake and regulates osmotic pressure, helping to maintain plant health. Potassium fertilizers are classified into two categories: chloride-based and chloride-free. Potassium sulfate, due to its lack of chloride, is a suitable option for chloride-sensitive crops such as tea, potatoes, and sugar beets. Meanwhile, hyperspectral imaging has emerged as an innovative technique with broad applications in detecting chemical parameters, assessing quality, and analyzing the purity of agricultural and food products. This study utilizes hyperspectral image processing technology to determine the pH level of potassium sulfate.Materials and MethodsThe present study was conducted in the Image Processing Laboratory at the Ilam University, Iran. To determine the pH level of potassium sulfate, four different levels of 2.5, 2.6, 2.8, and 2.9 were considered. The pH measurement was performed in the laboratory using a flame photometer. The required images were obtained through hyperspectral imaging using the line-scan method. For each pH level, three samples were obtained and six hyperspectral images were captured for each sample, resulting in 18 images per pH level and a total of 72 hyperspectral images for each pH level. MATLAB software was used for the analysis and processing of these images. The image processing stage included wavelength selection, feature extraction, and feature selection. Finally, the selected features were classified using an artificial neural network.Results and DiscussionPrincipal Component Analysis performed on the hyperspectral image channels of potassium sulfate revealed significant variations in the principal component values across different pH levels. This finding indicates that pH conditions exert a considerable influence on the spectral response of the samples. Based on the prominent peaks obtained from the analysis, the most relevant channels were identified, and their corresponding wavelengths were determined as the optimal spectral bands. The selected channels for the four pH levels were 65, 327, 334, 482, 510, 607, and 644, with their corresponding effective wavelengths being 453.32, 669.95, 675.74, 798.11, 821.26, 901.47, and 932.06 nm, respectively. To extract discriminative spectral information, six features were computed from each of the selected wavelengths. Consequently, a total of 42 features were obtained, which were subsequently employed in the classification process of different pH levels. The confusion matrices of the classification model based on the artificial neural network were obtained to evaluate the model's accuracy. The classification accuracy for detecting the pH level of potassium sulfate was 98.6% with effective features and 97.2% without them.ConclusionThe results of this study demonstrated the high potential of hyperspectral imaging technology combined with the artificial neural network classification method, using strategies with and without effective feature selection, in detecting the pH level of potassium sulfate. The proposed method offers several advantages over laboratory-based approaches, such as being non-destructive, having high speed, and being cost-effective. It is suggested to explore other methods for classifying hyperspectral images for determining the pH level of potassium sulfate. The proposed method in this study could also be applied in the future to identify various chemical elements in potassium sulfate.
Design and Construction
A. Mohammadi; K. Kheiralipour; B. Ghamari; A. Jahanbakhshi; R. Shahidi
Abstract
IntroductionThe permissible exposure time to vibration for the operator is one of the key factors in maintaining the operator's health while optimizing machinery and equipment. The tractor studied was the ITM475, manufactured in Iran. The purpose of this study was to calculate the operator's permissible ...
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IntroductionThe permissible exposure time to vibration for the operator is one of the key factors in maintaining the operator's health while optimizing machinery and equipment. The tractor studied was the ITM475, manufactured in Iran. The purpose of this study was to calculate the operator's permissible vibration exposure time while using the tractor to ensure the driver can maintain good bodily health.Materials and MethodsIn this study, experiments were conducted using a 3-axis vibration meter based on the ISO 2631 standard. The obtained data were analyzed through a factorial experiment using 18 treatments and 3 replications. The factors studied were engine rotation speed (at three levels of 1000, 1500, and 2000 rpm), road type (dirt and asphalt), and gear position (at three levels of 1, 2, and 3).Results and DiscussionVarious total vibration models were obtained for the tractor, and their determination coefficient varied from 90.11% for gear No. 3 on an asphalt road to 100% for gear No. 1 on an asphalt road and gear No. 2 on a dirt road. The maximum whole-body vibration, and consequently the minimum permissible exposure time, was observed for gear No. 3 at an engine rotation speed of 2000 rpm on a dirt road, which was 1.49 and 1.16 hours, respectively.ConclusionThe maximum whole-body vibration experienced during an 8-hour tractor-driving session was measured at 0.85 m s-2. It is important to note that the permissible exposure time decreases as vibration levels increase, and it reaches a limit of 1.16 hours. To ensure drivers adhere to these permissible exposure times across various driving conditions, measures must be implemented to reduce tractor vibration and minimize its transmission to the driver. By reducing overall tractor vibration and minimizing its impact on the driver, it becomes possible to increase the permissible exposure time for drivers.
Image Processing
A. Jahanbakhshi; K. Kheiralipour
Abstract
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 ...
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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. Conclusion 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.
