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

Plant diseases are a major concern in the agricultural sector; accordingly, it is very important to identify them automatically. In this study, we propose an improved deep learning-based multi-plant disease recognition method that combines deep features extracted by deep convolutional neural networks and k-nearest neighbors to output similar disease images via query image. Powerful, deep features were leveraged by applying fine-tuning, an existing method. We used 14,304 in-field images with six diseases occurring in apples and pears. As a result of the experiment, the proposed method had a 14.98% higher average similarity accuracy than the baseline method. Furthermore, the deep feature dimensions were reduced, and the image processing time was shorter (0.071–0.077 s) using the proposed 128-sized deep feature-based model, which processes images faster, even for large-scale datasets. These results confirm that the proposed deep learning-based multi-plant disease recognition method improves both the accuracy and speed when compared to the baseline method.

Improved Multi-Plant Disease Recognition Method Using Deep Convolutional Neural Networks in Six Diseases of Apples and Pears