AuToDiDAC: Automated tool for disease detection and assessment for cacao black pod rot
College
College of Computer Studies
Department/Unit
Software Technology
Document Type
Article
Source Title
Crop Protection
Volume
103
First Page
98
Last Page
102
Publication Date
1-1-2018
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. © 2017
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Digitial Object Identifier (DOI)
10.1016/j.cropro.2017.09.017
Recommended Citation
Tan, D., Leong, R. F., Laguna, A. B., Ngo, C. M., Lao, A., Amalin, D. M., & Alvindia, D. G. (2018). AuToDiDAC: Automated tool for disease detection and assessment for cacao black pod rot. Crop Protection, 103, 98-102. https://doi.org/10.1016/j.cropro.2017.09.017
Disciplines
Computer Sciences
Keywords
Phytophthora pod rot of cacao; Cacao—Diseases and pests
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