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Rice, obtained through the processing of paddy, is one of the most widely consumed food products globally. However, diseases affecting rice plants, particularly those occurring on the rice leaves, pose significant challenges for farmers. The identification of rice plant diseases demands specialized knowledge, making it a complex issue to tackle. Often, due to insufficient understanding, farmers misdiagnose diseases and apply incorrect treatments. Rapid and accurate disease diagnosis plays a pivotal role in enhancing healthy and productive crop cultivation. To address this problem, a deep learning-based model was developed to detect rice plant diseases. The model was trained on a dataset containing four different rice plant diseases and achieved a successful outcome with a loss value of 0.0014. Additionally, four different deep learning algorithms were used to create models through transfer learning with pre-trained ImageNet models, and a comparison of their performance was presented. The most successful model was obtained using the VGG16 transfer learning architecture. Experimental results in this study demonstrate that the proposed transfer learning method can effectively recognize rice leaf diseases, providing a reliable approach for identifying leaf diseases in various plants. [ABSTRACT FROM AUTHOR]

With the rapid advancement of drone technologies, the use of drones has become of significant importance, particularly raising serious concerns in the areas of security and privacy. Deep learning and transfer learning artificial intelligence techniques hold promise for drone detection. However, to successfully apply these techniques, the need to develop new and efficient solutions for accurately detecting complex aerial conditions, variable speeds, and highly maneuverable drones is inevitable. In this study, the performance of training models using the EfficientNet model for drone detection was compared, and the challenges encountered were discussed, offering a perspective on potential future successes. According to the obtained results, when more layers are frozen in the transfer learning method, the required GPU memory for training decreases, leading to reduced GPU usage. This indicates that models trained with larger image sizes can be trained more quickly. Deep learning methods require more data and GPU resources, which extend the training time. In the conducted experiments, the best success rate achieved by a model trained with the deep learning method was 97.3%, while the model trained using the transfer learning method achieved the highest success rate of 99.7%. This demonstrates that transfer learning achieves higher accuracy with less data. However, the success rate obtained through the deep learning method is also considered quite satisfactory. [ABSTRACT FROM AUTHOR]