  Soybean is one of the ten greatest crops in the world, answering for
billion-dollar businesses every year. This crop suffers from insect herbivory
that costs millions from producers. Hence, constant monitoring of the crop
foliar damage is necessary to guide the application of insecticides. However,
current methods to measure foliar damage are expensive and dependent on
laboratory facilities, in some cases, depending on complex devices. To cope
with these shortcomings, we introduce an image processing methodology to
measure the foliar damage in soybean leaves. We developed a non-destructive
imaging method based on two techniques, Otsu segmentation and Bezier curves, to
estimate the foliar loss in leaves with or without border damage. We
instantiate our methodology in a mobile application named BioLeaf, which is
freely distributed for smartphone users. We experimented with real-world leaves
collected from a soybean crop in Brazil. Our results demonstrated that BioLeaf
achieves foliar damage quantification with precision comparable to that of
human specialists. With these results, our proposal might assist soybean
producers, reducing the time to measure foliar damage, reducing analytical
costs, and defining a commodity application that is applicable not only to soy,
but also to different crops such as cotton, bean, potato, coffee, and
vegetables.
