Plants are one of the most important components of the environment. Millions of people are undernourished because of global warming whose adverse effects such as drought has made it difficult for sustainable crop breeding programs. This paper is aimed to propose and test computer vision and machine learning image-based methods precisely convolutional neural networks; for a benchmark suggested by the International Plant Phenotyping Network to help researchers, plant breeders choose desirable crop traits, and link them to specific genes that helped in the production of viable plants that could withstand harsher environmental conditions. Also as a first of its kind in Turkey and its environ, this paper is aimed to provide a ground base for future research in this area of agriculture. The benchmark chosen is the classification of mutants' benchmark (plant disease detection). In this paper, the dataset chosen was two of the main cash crops that can be found in Turkey were used: Maize and Grapes. Three different plant diseases affecting Grape and Maize were used respectively and a class of healthy grape and maize annotated images were added amounting to a total of 8 different classes and 1600 annotated images for both training and testing for the custom convolutional neural network to be proposed. The results show that the custom model achieved 97.03 % accuracy on the test dataset after training. The research thus concluded that, the custom model performed better than most currently used convolutional neural network models and can be used as a basis for further research in the field of image detection.

Post-traumatic stress disorder (PTSD) is defined as a traumatic injury developed after facing or witnessing a life-threatening experience or event such as a natural disaster, a pandemic, a serious accident, a terrorist act, war/combat, rape or other violent personal assault. Machine Learning (ML) has been widening its scope on psychological and physical healthcare for a decade by predicting detecting, personalizing, digitalizing, preventing risks, monitoring, and classifying PTSD based clinical mental diseases. In this study, we predict PTSD scores of the participants obtained from Mississippi-Civilian Version Scale and DSM-5 (PCL-5) Scale by applying ML. For our experiments we used the following methods namely k-nearest neighbor (k-nn), support vector machine (SVM), decision tree (DT), Gaussian Naive Bayes (GNB) and artificial neural networks (ANN). According to the comparison of the prediction results Considering PTSD prediction classification performance results for Mississippi (Civilian version) scale data set in comparison to the above mentioned methods, ANN offers the best prediction in terms of accuracy, F1 score and recall. However, Gaussian Naive Bayes (GNB) gives the best prediction score in terms of precision. On the other hand, when we applied all these methods to DSM-5 (PCL-5) scale data set, we have observed that ANN offers the best prediction in terms of accuracies, F1 score and precision. Nevertheless, in terms of recall Gaussian Naive Bayes (GNB) gives the best prediction score. By applying all the methods to these two different data sets and comparing the results, we demonstrate which method can be more efficient in prediction, diagnosis and monitoring the patients with PTSD.