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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper presents results on the detection and identification mango fruitsfrom colour images of trees. We evaluate the behaviour and the performances ofthe Faster R-CNN network to determine whether it is robust enough to "detectand classify" fruits under particularly heterogeneous conditions in terms ofplant cultivars, plantation scheme, and visual information acquisitioncontexts. The network is trained to distinguish the 'Kent', 'Keitt', and"Boucodiekhal" mango cultivars from 3,000 representative labelled fruitannotations. The validation set composed of about 7,000 annotations was thentested with a confidence threshold of 0.7 and a Non-Maximal-Suppressionthreshold of 0.25. With a F1-score of 0.90, the Faster R-CNN is well suitableto the simple fruit detection in tiles of 500x500 pixels. We then combine amulti-tiling approach with a Jaccard matrix to merge the different parts ofobjects detected several times, and thus report the detections made at the tilescale to the native 6,000x4,000 pixel size images. Nonetheless with a F1-scoreof 0.56, the cultivar identification Faster R-CNN network presents somelimitations for simultaneously detecting the mango fruits and identifying theirrespective cultivars. Despite the proven errors in fruit detection, thecultivar identification rates of the detected mango fruits are in the order of80%. The ideal solution could combine a Mask R-CNN for the imagepre-segmentation of trees and a double-stream Faster R-CNN for detecting themango fruits and identifying their respective cultivar to provide predictionsmore relevant to users' expectations.

Deep Mangoes: from fruit detection to cultivar identification in colour images of mango trees