Image Processing
H. Payman; A. Bakhshipour Ziaratgahi; A. A. Jafari
Abstract
Introduction: 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 ...
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Introduction: 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.Materials and Methods: 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 and Discussion: 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<0.01) was discovered and revealed between rice blast and rice brown spot stains. The developed algorithm was capable of distinguishing between disease symptoms with an exactness of over 96.6%. This means that of the 60 samples (30 samples rice blast and 30 samples of rice brown spot); only two were placed in the wrong category.Conclusions: It was concluded from this study that image processing technique can not only accurately determine whether the rice is healthy or infected but also can determine the type of plant disease with reliable precision. Considering the fact that plant diseases spread area by area through the field, early and accurate diagnosis of plant diseases in a part of a farm - which is provided by image processing techniques and machine vision systems – is very useful for timely and effective disease treatment which in turn leads to lower crop losses. Also, by using the site-specific chemical application technologies, the need for chemicals can be minimized, an important factor that can considerably reduce the costs.
B. Emadi; A. Nikkhah; M. Khojastehpour; H. Payman
Abstract
In this study, the energy and economic analysis of peanut production in Guilan province of Iran was studied. Data were collected from questionnaires of 75 farmers. The data were collected from three farm size categories namely: 0.1–0.5 ha, 0.5-1 ha and larger than 1 ha. The results revealed that 19407.36 ...
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In this study, the energy and economic analysis of peanut production in Guilan province of Iran was studied. Data were collected from questionnaires of 75 farmers. The data were collected from three farm size categories namely: 0.1–0.5 ha, 0.5-1 ha and larger than 1 ha. The results revealed that 19407.36 MJ ha-1 energy input was totally consumed. The highest share of energy consumption belonged to diesel fuel (50.05%) followed by chemical fertilizers (19.14%). The mean difference of energy inputs including machinery, diesel fuel and electricity among different sizes of farms was significant at the 5% level. The average energy efficiency in different farm size categories including less than 0.5 ha, 0.5-1 ha and more than 1 ha were 3.67, 4.02 and 4.12, respectively. The energy productivity of these sizes was calculated as 0.155, 0.169 and 0.174 kg MJ-1, respectively. The Cobb-Douglas model results showed that the effects of inputs including human labor, machinery, chemical fertilizers and electricity on the yield were positive, while the effect of inputs including seed, diesel fuel and chemicals on peanut yield were negative. The benefit-cost ratio was calculated as 1.82. Farmers with a farm larger than 1 ha used the least amount of energy and input costs.
