Computer technology and software are widely used in every multi-discipline field. Geomatics engineering can be seen as a pioneer of these disciplines especially in photogrammetry and image processing. Photogrammetry is a method where geometric parameters of objects on digitally captured images are determined and make measurements on them. Capturing the digital images and photogrammetric processing include several fully defined stages, which allows to generate three-dimension or two-dimension digital models of the body as an end product. The aim of this study is to predict Holstein cows’ live weight via artificial neural network whose body dimensions were determined with photogrammetry method. The body dimensions to be used in this study are obtained metric from analysis of cows’ images captured by synchronized three-dimension camera environment from different aspects. Wither height, hip height, body length, hip width of cows determined with photogrammetry. Artificial neural network prediction model was developed by using these body measurements. Dataset is divided into two after preprocessing as training and testing dataset. Different structured artificial neural network models are generated and the artificial neural network model which has the best performance is determined. Then with this artificial neural network model live weight of animals is estimated by using measurements obtained from images. After comparison of estimated live weights and weights obtained from scale, correlation coefficient is found (R=0.995). The statistical analysis shows that both groups are meaningful and artificial neural network can be used in live weight prediction safely.

The precise estimation of solar radiation is of great importance in solar energy applications with respect to installation and capacity. In estimate modelling on selected target locations, various computer-based and experimental methods and techniques are employed. In the present study, the Multilayer Feed-Forward Neural Network (MFFNN), -Nearest Neighbors (-NN), a Library for Support Vector Machines (LibSVM), and M5 rules algorithms, which are among the Machine Learning (ML) algorithms, were used to estimate the hourly average solar radiation of two geographic locations on the same latitude. The input variables that had the most impact on solar radiation were identified and grouped as a result of 29 different applications that were developed by using 6 different feature selection methods with Waikato Environment for Knowledge Analysis (WEKA) software. Estimation models were developed by using the selected data groups and all input variables for each target location. The results show that the estimations developed with the feature selection method were more successful for target locations, and the radiation potentials were similar. The performance of the estimation models was evaluated by comparing each model with different statistical indicators and with previous studies. According to the RMSE, MAE, , and SMAPE statistical scales, the results of the most successful estimation models that were developed with MFFNN were 0.0508-0.0536, 0.0341-0.0352, 0.9488-0.9656, and 7.77%-7.79%, respectively.

The purity of the seeds is one of the important factors that increase the yield. For this reason, the classification of maize cultivars constitutes a significant problem. Within the scope of this study, six different classification models were designed to solve this problem. A special dataset was created to be used in the models designed for the study. The dataset contains a total of 14,469 images in four classes. Images belong to four different maize types, BT6470, CALIPOS, ES_ARMANDI, and HIVA, taken from the BIOTEK company. AlexNet and ResNet50 architectures, with the transfer learning method, were used in the models created for the image classification. In order to improve the classification success, LSTM (Directional Long Short‐Term Memory) and BiLSTM (Bi‐directional Long Short‐Term Memory) algorithms and AlexNet and ResNet50 architectures were hybridized. As a result of the classifications, the highest classification success was obtained from the ResNet50+BiLSTM model with 98.10%.