Supervised Model for the Detection of Coffee Leaf Diseases by Image Analysis
Keywords:
Coffee, coffee rust, fuzzy inference system, image analysis, machine learningAbstract
An alternative model is presented for the early detection of coffee leaf rust disease, since this disease usually causes yield losses of up to 30% during the pandemic season, considering that the traditional detection method known as direct observation requires monetary resources that the farmer does not have. There are several technological alternatives to detect plant diseases in a short time, which are accurate but not very interpretable, therefore a supervised image analysis model is generated at pixel level according to the EG and GCC color indices to detect coffee leaf rust based on a fuzzy inference system, being this developed under experimental and prototype-based methodologies. The model obtained an accuracy of 93.75%, being considered effective and ready to be tested in uncontrolled environments, where the GCC color index presents a better discrimination of the state of a plant against the EG.
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