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

Plant diseases and poisonous insects are major threats to agriculture. As a result, detecting and diagnosing these illnesses as soon as feasible is critical. The ongoing development of major deep learning techniques has substantially aided in the diagnosis of plant leaf diseases, providing a potent instrument with incredibly exact results. Deep learning algorithms, on the other hand, are dependent on the quality and quantity of labelled data used for training. The lightweight parallel deep convolutional neural network is described in this study for detecting plant leaf disease. In addition, the Generative Adversarial Neural Network is introduced for creating synthetic data in order to overcome the data scarcity problem caused by uneven dataset size. The experimental results for two-class, six-class and ten-class disease identification of tomato plant samples from the Plant Village dataset are provided. The effectiveness of the proposed model is assessed using numerous performance measures, including accuracy, recall, precision and F1-score, and compared to known state-of-the-art approaches for tomato plant leaf disease detection. The proposed system provides better accuracy (99.14%, 99.05%, 98.11% accuracy for the 2-class, 6-class and 10-class) for tomato leaf disease detection compared with traditional existing approaches.

Detection of leaf disease in tomato plants using a lightweight parallel deep convolutional neural network