Pathogen Identification using Linear Regression and Convolutional Neural Networks

With the increase in awareness regarding conservation of forests, we must be wary to preserve them sustainably from potential pathogens. Statistics tells us that the number of trees that we lose every year due to pathogen attacks is huge and thus requires a machine learning model to identify the presence of pathogens to significantly reduce the number of deaths per year. TIn this paper we have done a cumulative study about the efficiency of two different models namely Linear Regression and CNN(Convolutional Neural Networks) and have achieved the following accuracies with respect to the actual data. For Linear Regression we have achieved an accuracy of 65.71% and an accuracy of 80.85% for CNN. Further analysis of various metrics like RMS(Root Mean Square) value, MAE(Mean Absolute Error) and MSE(Mean Squared Value) is done for both the models.