Deep learning-based segmentation of multiple species of weeds and corn crop using synthetic and real image datasets
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Author/sPicon, Artzai; San-Emeterio, Miguel G.; Bereciartua-Perez, Arantza; Klukas, Christian; Eggers, Till; [et al.]
Convolutional neural network
Plant safety digitalization
Weed semantic segmentation
Weeds compete with productive crops for soil, nutrients and sunlight and are therefore a major contributor to crop yield loss, which is why safer and more effective herbicide products are continually being developed. Digital evaluation tools to automate and homogenize field measurements are of vital importance to accelerate their development. However, the development of these tools requires the generation of semantic segmentation datasets, which is a complex, time-consuming and not easily affordable task. In this paper, we present a deep learning segmentation model that is able to distinguish between different plant species at the pixel level. First, we have generated three extensive datasets targeting one crop species (Zea mays), three grass species (Setaria verticillata, Digitaria sanguinalis, Echinochloa crus-galli) and three broadleaf species (Abutilon theophrasti, Chenopodium albums, Amaranthus retroflexus). The first dataset consists of real field images that were manually annotated. ...