عادل بخشی پور زیارتگاهی

Tea is one of the strategic products in north of Iran. The tea produced in tea factories have different qualities as it is affected by various factors such as weather conditions during growth, soil, harvest time, as well as processing and preparation methods. In addition to its appearance, other essential properties of tea are its chemical compounds and aromatic characteristics. Investigating new and accurate methods for tea quality assessment has a significant effect on the development of tea processing industries. In this research, an electronic nose system was used to extract the characteristics of tea aroma and applying of these features for qualitative classification of black tea. Extracted Features from a sensor array, including ten different metal oxide gas sensors (MOS) were used for classification of five qualitative categories of black tea by means of chemometric methods. Results showed that the best classification performance was obtained by Artificial Neural Network (ANN) with a total classification accuracy of 88.00%. Also, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) resulted in accuracies of 78.00% and 86.67% respectively. Based on the results of Principle Components Analysis (PCA), it was found that MQ7 and MQ2 sensors had the highest effect on the separation of different classes of tea. Generally, the performance of electronic nose system was suitable for qualitative classification of Iranian black tea.

Sugar beet (Beta vulgaris L.) as the second most important world’s sugar source after sugarcane is one of the major industrial crops. The presence of weeds in sugar beet fields, especially at early growth stages, results in a substantial decrease in the crop yield. It is very important to efficiently eliminate weeds at early growing stages. The first step of precision weed control is accurate detection of weeds location in the field. This operation can be performed by machine vision techniques.
Hough transform is one of the shape feature extraction methods for object tracking in image processing which is basically used to identify lines or other geometrical shapes in an image. Generalized Hough transform (GHT) is a modified version of the Hough transform used not only for geometrical forms, but also for detecting any arbitrary shape. This method is based on a pattern matching principle that uses a set of vectors of feature points (usually object edge points) to a reference point to construct a pattern. By comparing this pattern with a set pattern, the desired shape is detected. The aim of this study was to identify the sugar beet plant from some common weeds in a field using the GHT.
Images required for this study were taken at the four-leaf stage of sugar beet as the beginning of the critical period of weed control. A shelter was used to avoid direct sunlight and prevent leaf shadows on each other. The obtained images were then introduced to the Image Processing Toolbox of MATLAB programming software for further processing.
Green and Red color components were extracted from primary RGB images. In the first step, binary images were obtained by applying the optimal threshold on the G-R images.
A comprehensive study of several sugar beet images revealed that there is a unique feature in sugar beet leaves which makes them differentiable from the weeds. The feature observed in all sugar beet plants at the four-leaf stage was a stretched S-shaped curve at the junction of the leaf and petiole. This unique shape characteristic was used as the pattern for sugar beet detection using GHT. To implement the Hough transform in the images, a 50-member group of samples was prepared from S-shaped curve to build appropriate patterns. Desired features for the Hough transformation were extracted from the patterns. In the next step, the attempts were made to find the images for the shapes similar to each of the patterns.
Plants were thoroughly separated from soil and residues. The accuracy of segmentation algorithm was achieved by almost 100%.
The accuracy of the generalized Hough algorithm was evaluated in two stages. In the first stage, the algorithm accuracy was assessed in detecting patterns in the images. Results showed that the accuracy of the algorithm was 96.21%. In the second stage, the algorithm was evaluated for some other test images, whereas the algorithm achieved an overall accuracy of 91.65%. In some cases, the presence of a large overlap between objects in the image reduced the detection accuracy. This was because of two main reasons; 1) high interference and ambiguity in the object edges, so that Hough transform was not able to detect the predefined patterns in the objects and, 2) weeds highly overlapped with sugar beet plants and thereby they were wrongly detected as sugar beet. However, since there is no or little interference between plants at the four-leaf stage, this interference can be eliminated by morphological operations. Due to this fact, it can be said that the results of GHT algorithm are acceptable for the detection of sugar beet in the plants close to four-leaf stage.
A special feature in the shape of sugar beet leaves was used as a criterion to distinguish between sugar beet and weeds. The results showed that by quantifying this special feature, which is an S-shaped curve near the petioles connection of beet leaves, sugar beet can be discriminated from weeds with an accuracy of 91.65 %. Recalled that this feature is a shape characteristic, therefore, the generalized Hough algorithm must be applied prior to plant canopy development, which is consistent with the critical period of weed control in sugar beet fields.

Rice is a very important staple food crop provides more than half of the world caloric supply. Rice diseases lead to significant annual crop losses, have negative impacts on quality of the final product and destroy plant variety. Rice Blast is one of the most widespread and most destructive fungal diseases in tropical and subtropical humid areas, which causes significant decrease in the amount of paddy yield and quality of milled rice.
Brown spot disease is another important fungal disease in rice which infects the plant during the rice growing season from the nursery period up to farm growth stage and productivity phase. The later the disease is diagnosed the higher the amount of chemicals is needed for treatment. Due to high costs and harmful environmental impacts of chemical toxins, the accurate early detection and treatment of plant disease is seemed to be necessary.
In general, observation with the naked eye is used for disease detection. However, the results are indeed depend on the intelligence of the person performing the operation. So usually the accurate determination of the severity and progression of the disease can’t be achieved. On the other side, the use of experts for continuous monitoring of large farms might be prohibitively expensive and time consuming. Thus, investigating the new approaches for rapid, automated, inexpensive and accurate plant disease diagnosis is very important.
Machine vision and image processing is a new technique which can capture images from a scene of interest, analyze the images and accurately extract the desired information. Studies show that image processing techniques have been successfully used for plant disease detection.
The aim of this study was to investigate the ability of image processing techniques for diagnosing the rice blast and rice brown spot.
The samples of rice leaf infected by brown spot and rice blast diseases were collected from rice fields and the required images were obtained from each sample.The images of infected leaves were then introduced to image processing toolbox of MATLAB software. The RGB images were converted to gray-scale. Using a suitable threshold, the leaf surface was segmented from image background and the first binary image was achieved. Leaf image with zero background pixels was obtained after multiplying the black-and-white image to original color image. The resulting image was transformed to HSV color space and the Hue color component was extracted. The final binary image was created by applying an appropriate threshold on the image that obtained from Hue color component.
As there was a high color similarity between the symptoms of two diseases, it was not possible to use Hue color component to distinguish between them. Therefore the shape processing was applied.
Four dimensionless morphological features such as Roundness, Aspect Ratio, Compactness and Area Ratio were extracted from stain areas and based on these features, disease type diagnosis was performed.
Results showed that the proposed algorithm successfully diagnosed the diseases stains on the rice leaves. A detection accuracy of 97.4±1.4 % was achieved.
Regarding the results of t-test, among the extracted shape characteristics, only in the case of Area Ratio, there was no significant difference between two disease symptoms. While in the case of Roundness, Aspect Ratio and Compactness, a highly significant difference (P

Downy Mildew of cucurbits is one of the most important diseases of cucumber in humid areas and greenhouses. It can lead to significant damages to the quality and quantity of the product, if not diagnosed on time. In this study, the possibility of using image processing for determining the downy mildew of greenhouse cucumber was investigated. The captured images from cucumber leaves at several stages of disease severity were processed in Image Processing toolbox of MATLAB programming software. Color images were transferred to several color spaces and then color components were examined by discriminant analysis. Cr color component was determined to be suitable to detect disease spots in leaf and was used to develop the recognition algorithm. The accuracy of algorithm in terms of identify the infected areas of leaves was 97.4±1.4 percent. Discriminant analysis was also used to classify the severity of the disease. Results revealed that image processing is a suitable method for accurate diagnosis of downy mildew in greenhouse cucumber leaves. Discriminant analysis is also a useful tool to classify disease severity in images resulted from image processing.