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

In this paper, we present a novel approach to kiwi fruit flower detectionusing Deep Neural Networks (DNNs) to build an accurate, fast, and robustautonomous pollination robot system. Recent work in deep neural networks hasshown outstanding performance on object detection tasks in many areas. Inspiredthis, we aim for exploiting DNNs for kiwi fruit flower detection and presentintensive experiments and their analysis on two state-of-the-art objectdetectors; Faster R-CNN and Single Shot Detector (SSD) Net, and featureextractors; Inception Net V2 and NAS Net with real-world orchard datasets. Wealso compare those approaches to find an optimal model which is suitable for areal-time agricultural pollination robot system in terms of accuracy andprocessing speed. We perform experiments with dataset collected from differentseasons and locations (spatio-temporal consistency) in order to demonstrate theperformance of the generalized model. The proposed system demonstratespromising results of 0.919, 0.874, and 0.889 for precision, recall, andF1-score respectively on our real-world dataset, and the performance satisfiesthe requirement for deploying the system onto an autonomous pollinationrobotics system.

Deep Neural Network Based Real-time Kiwi Fruit Flower Detection in an Orchard Environment