This paper presents a novel methodology for plant disease detection using YOLOv8 (You Only Look Once version 8), a state-of-the-art object detection model designed for real-time image classification and recognition tasks. The proposed approach involves training a custom YOLOv8 model to detect and classify various plant conditions accurately. The model was evaluated using a testing subset to measure its performance in detecting different plant diseases. To ensure the model’s robustness and generalizability beyond the training dataset, it was further tested on a set of unseen images sourced from Google Images. This additional testing aimed to assess the model’s effectiveness in real-world scenarios, where it might encounter new data. The evaluation results were auspicious, demonstrating the model’s capability to classify plant conditions, such as diseases, with high accuracy. Moreover, the use of YOLOv8 offers significant improvements in speed and precision, making it suitable for real-time plant disease monitoring applications. The findings highlight the potential of this methodology for broader agricultural applications, including early disease detection and prevention.

This research investigation utilizes deep learning object detection algorithms to achieve accurate recognition of birds near airports, thereby addressing the limitations of manual bird detection at airports, including low accuracy slow speed, and the high cost of radar detection. The ultimate goal is to ensure the safe operation of civil aviation. The following are the primary enhancements: First, an ECA (Efficient Channel Attention) attention mechanism was added to the Neck to enhance the network's emphasis on important characteristics. This resulted in a notable improvement in accuracy while only changing a few parameters. Second, by adding branches with various receptive fields, the MBC3 (Muti Branch C3) module was created to improve the expressiveness of the model. Thirdly, the model's right width and depth parameters will be chosen by investigating the effects of various network widths and depths on model performance. Fourth, to solve the problem of feature loss in recognizing tiny bird targets, the SF-PAN (Shallow Feature - Path Aggregation Network) structure was proposed. The model was evaluated using metrics such as mAP@50, FPS, precision, recall, and computational complexity on a test set derived from the dataset. Results show that the enhanced YOLOv8 achieves a mAP@50 of 83.1% and a speed of 31 FPS, a 2.5% improvement in accuracy and a 7 FPS increase over the baseline YOLOv8, while reducing parameters and weight size by approximately 48%. Comparative experiments further validate the model’s superiority over existing algorithms in terms of accuracy and resource efficiency. This upgraded YOLOv8 provides a novel, real-time solution for precise bird detection in challenging airport environments, ensuring safer civil aviation operations.