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• W2346867319 abstract "The detection of diseases is of vital importance to increase the productivity of soybean crops. The presence of the diseases is usually conducted visually, which is time-consuming and imprecise. To overcome these issues, there is a growing demand for technologies that aim at early and automated disease detection. In this line of work, we introduce an effective (over 98% of accuracy) and efficient (an average time of 0.1 s per image) method to computationally detect soybean diseases. Our method is based on image local descriptors and on the summarization technique Bag of Visual Words. We tested our approach on a dataset composed of 1200 scanned soybean leaves considering healthy samples, and samples with evidence of three diseases commonly observed in soybean crops – Mildew, Rust Tan, and Rust RB. The experimental results demonstrated the accuracy of the proposed approach and suggested that it can be easily applied to other kinds of crops." @default.
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• W2346867319 date "2016-07-01" @default.
• W2346867319 modified "2023-09-30" @default.
• W2346867319 title "Local descriptors for soybean disease recognition" @default.
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• W2346867319 doi "https://doi.org/10.1016/j.compag.2016.04.032" @default.
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