@article{4427,
  author = {Jesus Carmona-Frausto, Adriana Mexicano-Santoyo, Kevin Bee-Cruz, Lilia Garcia-Mundo, Lilia Lilia Mexicano-Santoyo},
  title = {Optimization and Artificial Vision: Innovative Tool for Detecting Huanglongbing in Citrus},
  journal = {Digital Signal Processing and Artificial Intelligence for Automatic Learning},
  year = {2025},
  volume = {4},
  number = {1},
  doi = {https://doi.org/10.6025/dspaial/2025/4/1/1-15},
  url = {https://www.dline.info/dspai/fulltext/v4n1/dspaiv4n1_1.pdf},
  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 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.},
}