Notice of Violation of IEEE Publication Principles: Grape Disease Detection Network Based on Multi-Task Learning and Attention Features

Notice of Violation of IEEE Publication Principles

"Grape Disease Detection Network Based on Multi-Task Learning and Attention Features,"
by R. Dwivedi, S. Dey, C. Chakraborty and S. Tiwari,
in IEEE Sensors Journal, vol. 21, no. 16, August 2021, pp. 17573-17580

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.

This paper contains significant portions of content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
BR>"Strawberry Verticillium Wilt Detection Network Based on Multi-Task Learning and Attention,"
by X. Nie, L. Wang, H. Ding and M. Xu,
in IEEE Access, vol. 7, 2019, pp. 170003-170011


The disease-free growth of a plant is highly influential for both environment and human life. However, there are numerous plant diseases such as viruses, fungus, and micro-organisms that affect the growth and agricultural production of a plant. Grape esca, black-rot, and isariopsis are multi-symptomatic soil-borne diseases. Often, these diseases may cause leaves drop or sometimes even vanishes the plant/plant vicinity. Hence, early detection and prevention becomes necessary and must be treated on time for better grape growth and productivity. The state-of-the-art either involve classical computer vision techniques such as edge detection/segmentation or regression-based object detection applied over UAV images. In addition, the treatment is not viable until detected leaves are classified for actual disease/symptoms. This results in increased time and cost consumption. Therefore, in this paper, a grape leaf disease detection network (GLDDN) is proposed that utilizes dual attention mechanisms for feature evaluation, detection, and classification. At evaluation stage, the experimentation performed over benchmark dataset confirms that disease detection network could be fairly befitting than the existing methods since it recognizes as well as detects the infected/diseased regions. With the proposed disease detection mechanism, we achieved an overall accuracy of 99.93% accuracy for esca, black-rot and isariopsis detection.