Abstract Huanglongbing (HLB), or “citrus greening,” is a bacterial disease transmitted by the Asian citrus psyllid (Diaphorina citri) that poses a severe threat to global citrus production, causing significant economic losses. This study explores advanced detection methods based on artificial vision and machine learning, such as hyperspectral cameras and drones, achieving accuracies of up to 99.72%. These technologies enable more efficient and scalable early detection... Huanglongbing (HLB), or “citrus greening,” is a bacterial disease transmitted by the Asian citrus psyllid (Diaphorina citri) that poses a severe threat to global citrus production, causing significant economic losses. This study explores advanced detection methods based on artificial vision and machine learning, such as hyperspectral cameras and drones, achieving accuracies of up to 99.72%. These technologies enable more efficient and scalable early detection compared to traditional methods like PCR and visual inspections. Despite challenges in implementation and cost, these innovations offer promising solutions to mitigate the impact of HLB and safeguard the global citrus industry. Read More Read Less

Abstract To address the challenges of complex backgrounds and incomplete trajectory capture due to the fast move- ment of the target, this study proposes a data capture method for volleyball player training trajectories based on the mean shift algorithm. The human body model is considered a skeleton model with 51 degrees of freedom and 16 joints to digitize the training trajectory data, and dimensionality... To address the challenges of complex backgrounds and incomplete trajectory capture due to the fast move- ment of the target, this study proposes a data capture method for volleyball player training trajectories based on the mean shift algorithm. The human body model is considered a skeleton model with 51 degrees of freedom and 16 joints to digitize the training trajectory data, and dimensionality reduction is applied to reduce compu- tational complexity. To reduce the dependency of the mean shift algorithm on environmental parameters, a probability density function from the gradient iterative estimation algorithm is selected, and the target’s color information is used as a feature to complete the trajectory data capture. The experiment demonstrates that the method can capture the motion of each athlete’s joint, achieving more accurate training trajectory data capture without depending on relevant parameters. Read More Read Less