In the original publication [1], reference number 11 (“Singh, A.K.; Sreenivasu, S.V.N.; Mahalaxmi, U.S.B.K.; Sharma, H.; Patil, D.D.; Asenso, E. Hybrid Feature-Based Disease Detection in Plant Leaf Using Convolutional Neural Network, Bayesian Optimized SVM, and Random Forest Classifier. J. Food Qual. 2022, 2022, 2845320”) should be changed. The reference has therefore been replaced in the References section and should read as follows:
“11. Sun, X.; Li, G.; Qu, P.; Xie, X.; Pan, X.; Zhang, W. Research on plant disease identification based on CNN. Cogn. Robot. 2022, 2, 155–163”.
Subsequently, a correction has been made to the Introduction, Paragraph 2. The related cited text should read as follows:
“Xuewei Sun et al. [11] used an optimized convolutional neural network, FL-EfficientNet, for plant disease image recognition. They improved the accuracy of complexity samples by adjusting network parameters, using the moving flip bottleneck convolutions and attention mechanisms to enhance feature extraction, etc. On the NPDD public dataset, the model achieved an accuracy of 99.72% in identifying 5 crops with 10 types of diseases, outperforming traditional feature extraction methods.”
The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.
Reference
- Yue, X.; Li, H.; Song, Q.; Zeng, F.; Zheng, J.; Ding, Z.; Kang, G.; Cai, Y.; Lin, Y.; Xu, X.; et al. YOLOv7-GCA: A Lightweight and High-Performance Model for Pepper Disease Detection. Agronomy 2024, 14, 618. [Google Scholar] [CrossRef] [Scilit]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).