English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Applying agrochemicals is the default procedure for conventional weed controlin crop production, but has negative impacts on the environment. Robots havethe potential to treat every plant in the field individually and thus canreduce the required use of such chemicals. To achieve that, robots need theability to identify crops and weeds in the field and must additionally selecteffective treatments. While certain types of weed can be treated mechanically,other types need to be treated by (selective) spraying. In this paper, wepresent an approach that provides the necessary information for effectiveplant-specific treatment. It outputs the stem location for weeds, which allowsfor mechanical treatments, and the covered area of the weed for selectivespraying. Our approach uses an end-to-end trainable fully convolutional networkthat simultaneously estimates stem positions as well as the covered area ofcrops and weeds. It jointly learns the class-wise stem detection and thepixel-wise semantic segmentation. Experimental evaluations on differentreal-world datasets show that our approach is able to reliably solve thisproblem. Compared to state-of-the-art approaches, our approach not onlysubstantially improves the stem detection accuracy, i.e., distinguishing cropand weed stems, but also provides an improvement in the semantic segmentationperformance.

Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming