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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The work proposed a method for recognising the green grapes in the orchard based on multi-source image fusion. First, the acquired multi-source images were denoised based on median filtering and wavelet transform. After extracting the feature points by the improved SURF (speeded up robust features) method, the registration was completed based on the consistency of feature offset and the affine relationship between images. The registered multi-source images were fused based on the CS (compressed sensing) and NSCT-DWT (non-down sampled contourlet transform-discrete wavelet transform). Then the MI-OPT (mutual-information optimal threshold) and the minimum circumscribed rectangle were used to segment the fused images and recognise fruits. The experimental results showed that the information of the target fruits in the fused images was complete. Therefore, compared with the K-means method using colour components of the visible light image and the OTSU (proposed by Nobuyuki Otsu and named after him) method based on near-infrared image, the fruit region obtained by the algorithm in the work was complete. On this basis, the average recognition rate of green grapes reached 92.1%.

A fruit recognition method of green grape images in the orchard