Correction to “Identification of Plant Species Using Convolutional Neural Network with Transfer Learning”

IF 1.1 4区 农林科学 Q3 PLANT SCIENCES Journal of Phytopathology Pub Date : 2025-03-28 DOI:10.1111/jph.70054
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Abstract

Arun, A., S. Sharma, B. Singh, and T. Hazra. 2025. “ Identification of Plant Species Using Convolutional Neural Network with Transfer Learning.” Journal of Phytopathology, 173, no. 1: e70032. https://doi.org/10.1111/jph.70032

In the above published article, several labeling errors were identified in figure captions and section references. These errors occurred in the Figures 14–18 and Sections 4.5.1 and 4.5.2 as follows:

In subsection 4.5.1 of “Performance Analysis of the Proposed Work” section, the reference to “Figure 14” was incorrect. This should refer to “Figure 18” instead.

We apologize for these errors.

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对“基于迁移学习的卷积神经网络识别植物物种”的修正
Arun, A., S. Sharma, B. Singh和T. Hazra, 2025。使用带有迁移学习的卷积神经网络识别植物物种。植物病理学杂志,第173期。1: e70032。https://doi.org/10.1111/jph.70032在上述发表的文章中,在图片标题和章节参考文献中发现了几个标签错误。这些错误发生在图14 - 18以及第4.5.1和4.5.2部分,具体如下:在“建议工程的性能分析”部分的第4.5.1小节中,对“图14”的引用不正确。这应该参考“图18”。我们为这些错误道歉。
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来源期刊
Journal of Phytopathology
Journal of Phytopathology 生物-植物科学
CiteScore
2.90
自引率
0.00%
发文量
88
审稿时长
4-8 weeks
期刊介绍: Journal of Phytopathology publishes original and review articles on all scientific aspects of applied phytopathology in agricultural and horticultural crops. Preference is given to contributions improving our understanding of the biotic and abiotic determinants of plant diseases, including epidemics and damage potential, as a basis for innovative disease management, modelling and forecasting. This includes practical aspects and the development of methods for disease diagnosis as well as infection bioassays. Studies at the population, organism, physiological, biochemical and molecular genetic level are welcome. The journal scope comprises the pathology and epidemiology of plant diseases caused by microbial pathogens, viruses and nematodes. Accepted papers should advance our conceptual knowledge of plant diseases, rather than presenting descriptive or screening data unrelated to phytopathological mechanisms or functions. Results from unrepeated experimental conditions or data with no or inappropriate statistical processing will not be considered. Authors are encouraged to look at past issues to ensure adherence to the standards of the journal.
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