Automated detection of Dubas palm infestation by image processing techniques has practical significance as it can improve agricultural efficiency, increase crop yield and quality, protect the environment, and provide data-driven insights. It also reduces the human effort required for pest control and enhances sustainability. In this study, we aimed to automate the detection of Dubas bug infestation in palm trees using deep learning with transfer learning residual neural networks. Based on four models: InceptionResNetV2, ResNet18, ResNet50, and ResNet101, the data used in this study were obtained by drone photography, many images were taken, and then the infected area was extracted. Using two types of data, 185 infected images and 185 health

Background/Aims: The nano-method has been used as a technique for creating novel, non-traditional antimicrobial agents. This effective method for treating infectious diseases has many advantages over conventional antibiotics, including increased efficacy against species that have developed drug resistance, and the ability to circumvent the development of resistance that disrupts a number of biological pathways. As a result, the objective of this study was to synthesize and characterize silver nanoparticles using phenolic compounds obtained from Camellia sinensis. The nanoparticles were then used as antibacterial agents on the multidrug resistant Staphylococcus aureus, as well as, biofilm formation mechanism were also investigated. M