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GitHub Repo https://github.com/shruti821/Leaf-Disease-Detection-Using-Image-Processing

shruti821/Leaf-Disease-Detection-Using-Image-Processing

Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases i.e. when they appear on plant leaves. This paper introduces an efficient approach to identify healthy and diseased or an infected leaf using image processing and machine learning techniques. Various diseases damage the chlorophyll of leaves and affect with brown or black marks on the leaf area. These can be detected using image prepossessing, image segmentation. Support Vector Machine (SVM) is one of the machine learning algorithms is used for classification. The Convolutional Neural Network (CNN) resulted in a improved accuracy of recognition compared to the SVM approach.

GitHub Repo https://github.com/arkidbera/Fission-product-release

arkidbera/Fission-product-release

Fission product release has been a matter of study since many years. Its importance lies in the fact that the release of radionuclides affect radiological impact due to a postulated accident scenario. The fission product gases released mainly consist of Xenon and Krypton, due to their low boiling point temperature and insolubility in the fuel matrix. These gases are released from fuel pellets due to the rupture of cladding to the coolant in the primary heat transport system. No standard method for calculation of fission product release gases has yet been found, due to the involvement of numerous factors, leading to variance in results. In our project, we have attempted to do the same, by creating a fission release model using various relations and carrying out certain computations on MATLAB, which includes vectorized implementations, iterative computations and plotting various curves to visualize the results obtained. The aim of our model is to find the fractional release of fission product gases with respect to fuel temperature, which depends on linear heat rating (i.e. power extracted per unit length of fuel pin) and burnup of the fuel. Our model also finds certain curves representing the behaviour of fractional release with respect to burnup, by varying grain radius and Diffusion Coefficient. The model has further been used as a tool for certain time varying analyses, where we have simulated a condition where the average temperature of the fuel pellets is changing with time, and thus, found the value of release fraction for those time instants. Finally, certain statistical analyses have been carried out, which include input parameter uncertainty, model parameter uncertainty and time-varying error analysis. We have used algorithms like Box-Muller transform in our study. Also, tolerance bands have been derived using confidence interval values for time-varying error analysis.