BibTeX for

AuToDiDAC: Automated Tool for Disease Detection and Assessment for Cacao Black Pod Rot

          
@article{TAN201898,
    title = “AuToDiDAC: Automated Tool for Disease Detection and Assessment for Cacao Black Pod Rot”,
    journal = “Crop Protection”,
    volume = “103”,
    pages = “98 - 102”,
    year = “2018”,
    issn = “0261-2194”,
    doi = “https://doi.org/10.1016/j.cropro.2017.09.017”,
    url = “http://www.sciencedirect.com/science/article/pii/S0261219417302867”,
    author = “Daniel Stanley Tan and Robert Neil Leong and Ann Franchesca Laguna and Courtney Anne Ngo and Angelyn Lao and Divina M. Amalin and Dionisio G. Alvindia”,
    keywords = “Cacao, Black pod rot, Defect segmentation, Infection level, Disease management tool, Decision support”,
    abstract = “Pest control strategies for crop diseases highly depend on visual inspection to assess the severity of the infection, which usually lead to inconsistencies: either over or under assessment. These inconsistencies could be attributed to the limitations of humans to perceive small differences. A more precise disease assessment is needed for better pest management decision, which will result to a more efficient utilization and allocation of resources for farm inputs. This translates to a better income for cacao farmers. This paper introduces a mobile application named AuToDiDAC or Automated Tool for Disease Detection and Assessment for Cacao Black Pod Rot (BPR). AuToDiDAC automatically detects, separates, and assesses the infection level of BPR in cacao through image processing and machine learning techniques. It gives the farmers the capacity to objectively monitor and report the infection level of the BPR compared to the common visual rating for plant disease level of infection. Pixel-level accuracy test of the tool showed an average of 84% accuracy on an independent test set of ten cacao pod images.”
}