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Abstract  A large percentage of Colombia’s economic activity corresponds to the agricultural sector. In this sector, plantains rank second in production and planted area. However this crop is affected by different diseases, among which The Black Sigatoka stands out, caused by the fungus Mycosphaerella fijiensis. The disease highly reduces the production level of the crop and although there are prevention measures that allow reducing the incidence of the disease, there’s a lack of support for small producers in Colombia, who do not have technological tools to support the disease detection processes. This article outlines the development of a support system for the detection of black sigatoka using digital images. For this, a characterization process of the agricultural user is carried out, then, a machine learning methodology is implemented to classify the disease on a mobile device. The support system is validated through laboratory tests, field tests and the feedback from the agricultural user.

Development of a digital image classification system to support technical assistance for Black Sigatoka detection