The impact of disease on strawberry quality and yield holds considerable significance, prompting researchers to explore effective methodologies for disease detection in strawberries. Among these, deep learning has emerged as a pivotal approach. In this regard, this research explored the utilization of transfer learning in deep convolutional neural networks (CNNs) to identify various strawberry diseases. Specifically, we utilized models pre-trained on the ImageNet dataset, namely VGG19, Inception V3, ResNet50, and DenseNet121 architectures, employing both fine-tuning and feature extraction techniques of transfer learning and consequently compared to the models without transfer learning. The target diseases for identification included angular leaf spot, anthracnose, gray mold, and powdery mildew on both fruit and leaves. The study outcomes revealed that Resnet-50 consistently achieved the highest accuracy across all three configurations, achieving its peak accuracy at 94.4 %, followed by Densenet-121 with an accuracy of 94.1 % attained through fine-tuning. These results highlighted the superior performance of fine-tuned models over using these models solely as feature extractors for identifying strawberry diseases. Furthermore, this study revealed that the application of transfer learning substantially reduced training time and resulted in a lower count of trainable parameters than models trained without transfer learning. These outcomes strongly endorse the practicality and effectiveness of employing transfer learning techniques for precise strawberry disease identification. Additionally, further research can explore the application of transfer learning to a broader range of crops and diseases, potentially enhancing agricultural disease detection methodologies.

中文翻译：

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使用深度卷积神经网络的迁移学习进行草莓病害检测

病害对草莓品质和产量的影响具有重要意义，促使研究人员探索草莓病害检测的有效方法。其中，深度学习已成为关键方法。在这方面，本研究探索了利用深度卷积神经网络（CNN）中的迁移学习来识别各种草莓疾病。具体来说，我们利用在 ImageNet 数据集上预训练的模型，即 VGG19、Inception V3、ResNet50 和 DenseNet121 架构，同时采用迁移学习的微调和特征提取技术，从而与没有迁移学习的模型进行比较。鉴定的目标病害包括果叶角斑病、炭疽病、灰霉病、白粉病。研究结果显示，Resnet-50 在所有三种配置中始终实现了最高准确度，达到了 94.4% 的峰值准确度，其次是 Densenet-121，通过微调实现了 94.1% 的准确度。这些结果凸显了微调模型比仅使用这些模型作为特征提取器来识别草莓疾病的优越性能。此外，这项研究表明，与未使用迁移学习训练的模型相比，迁移学习的应用大大减少了训练时间，并导致可训练参数的数量减少。这些结果有力地证明了采用迁移学习技术精确识别草莓病害的实用性和有效性。此外，进一步的研究可以探索迁移学习在更广泛的作物和疾病中的应用，从而有可能增强农业疾病检测方法。