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Traditionally, diagnosis and monitoring of agricultural diseases are carried out through on-site observation and inspection. These methods are time-consuming and may represent limited samples. Therefore, remote sensing technology has become an important tool in disease detection and monitoring in agriculture. In the research, Cercospora leaf spot (Cercospora beticola sacc.) disease, which cause significant economic losses in sugar beet production, were detected in the early stages using machine learning algorithms using non-invasive multispectral images taken with UAV under field conditions is intended to be determined. The research was fulfilled using images from the grower fields in two regions where the disease was observed intensively. Index value data from digital surface model maps created by processing the images taken were used as training and test data. Numerical data was tested using five different supervised machine learning methods. The success of the analyzed models in predicting disease formation from the index values obtained from the images taken and the physiological changes that occur before the disease agents appear on sugar beet leaves was over 70%. Among the models compared in the study, the k-nearest neighbor classifier (KNN) model gave the highest success in both diseases, with 83% accuracy and 76% and 86% f1-score values. The support vector machines model followed the KNN model with 77% accuracy, 75%, and 86% f1-score values. According to the results of the research, it has been revealed that plant diseases have the potential for pre-symptomatic detection, and by processing the images obtained with UAV-based MS images, it is possible to detect diseases in the early period.

A numerical investigation of the reflection and transmission of axial waves at stepped nanorods is presented. The scale dependent doublet mechanics theory is used in the analysis. The main difference of the doublet mechanics from other scale dependent models (stress gradient, strain gradient and couple stress theories) is its direct dependence to the micro/nano structure of the solid. Scale parameter is directly related to atomic structure of the material in doublet mechanics theory and it is assumed as carbon-carbon bond length in the present study. However, identification of scale parameters in other scale dependent theories is difficult compared to doublet mechanics theory. Governing equations of stepped nanorods are derived in the framework of doublet mechanics using the Hamilton Principle. The numerical results predicted by doublet mechanics are shown and compared with the classical elasticity.