Local descriptors for soybean disease recognition

•A novel approach is proposed for soybean disease recognition using leaf images.•It is based on local descriptors and bag-of-visual words.•Experimental results on an image dataset with 1200 samples validate its effectiveness.•Results also show that color increases the correct classification rate. 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 0.1s 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.