Wang Bin, Yang Hua, Zhang Shujuan, Li Lili
College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Plants (Basel). 2024 Jun 1;13(11):1535. doi: 10.3390/plants13111535.
In this study, our aim is to find an effective method to solve the problem of disease similarity caused by multiple diseases occurring on the same leaf. This study proposes the use of an optimized RegNet model to identify seven common apple leaf diseases. We conducted comparisons and analyses on the impact of various factors, such as training methods, data expansion methods, optimizer selection, image background, and other factors, on model performance. The findings suggest that utilizing offline expansion and transfer learning to fine-tune all layer parameters can enhance the model's classification performance, while complex image backgrounds significantly influence model performance. Additionally, the optimized RegNet network model demonstrates good generalization ability for both datasets, achieving testing accuracies of 93.85% and 99.23%, respectively. These results highlight the potential of the optimized RegNet network model to achieve high-precision identification of different diseases on the same apple leaf under complex field backgrounds. This will be of great significance for intelligent disease identification in apple orchards in the future.
在本研究中，我们的目标是找到一种有效的方法来解决同一叶片上出现多种病害导致的病害相似性问题。本研究提出使用优化的RegNet模型来识别七种常见的苹果叶片病害。我们对训练方法、数据扩充方法、优化器选择、图像背景等各种因素对模型性能的影响进行了比较和分析。研究结果表明，利用离线扩充和迁移学习对所有层参数进行微调可以提高模型的分类性能，而复杂的图像背景会显著影响模型性能。此外，优化后的RegNet网络模型对两个数据集都表现出良好的泛化能力，测试准确率分别达到93.85%和99.23%。这些结果凸显了优化后的RegNet网络模型在复杂田间背景下对同一苹果叶片上不同病害进行高精度识别的潜力。这对未来苹果园的智能病害识别具有重要意义。
