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Abstract：
Pepper leaf disease identification based on convolutional neural networks (CNNs) is one of the interesting research areas. However, most existing CNN-based pepper leaf disease detection models are suboptimal in terms of accuracy and computing performance. In particular, it is challenging to apply CNNs on embedded portable devices due to a large amount of computation and memory consumption for leaf disease recognition in large fields. Therefore, this paper introduces an enhanced lightweight model based on GoogLeNet architecture. The initial step involves compressing the Inception structure to reduce model parameters, leading to a remarkable enhancement in recognition speed. Furthermore, the network incorporates the spatial pyramid pooling structure to seamlessly integrate local and global features. Subsequently, the proposed improved model has been trained on the real dataset of 9183 images, containing 6 types of pepper diseases. The cross-validation results show that the model accuracy is 97.87%, which is 6% higher than that of GoogLeNet based on Inception-V1 and Inception-V3. The memory requirement of the model is only 10.3 MB, which is reduced by 52.31%-86.69%, comparing to GoogLeNet. We have also compared the model with the existing CNN-based models including AlexNet, ResNet-50 and MobileNet-V2. The result shows that the average inference time of the proposed model decreases by 61.49%, 41.78% and 23.81%, respectively. The results show that the proposed enhanced model can significantly improve performance in terms of accuracy and computing efficiency, which has potential to improve productivity in the pepper farming industry.
摘要：
基于卷积神经网络（CNN）的辣椒叶病识别是有趣的研究领域之一。然而,大多数现有的基于CNN的辣椒叶病检测模型在准确性和计算性能方面都是次优的。特别是,由于在大型领域中进行叶片病害识别需要大量的计算和内存消耗，因此在嵌入式便携式设备上应用CNN具有挑战性。因此,本文介绍了一种基于GoogLeNet架构的增强型轻量级模型。初始步骤涉及压缩Inception结构以减少模型参数，导致识别速度显着提高。此外，该网络结合了空间金字塔池化结构，以无缝地整合局部和全局特征。随后，所提出的改进模型已经在9183张图像的真实数据集上进行了训练，含有6种辣椒病。交叉验证结果表明,模型准确率为97.87%,比基于Inception-V1和Inception-V3的GoogLeNet高出6%。该模型的内存需求仅为10.3MB，减少了52.31%-86.69%，与GoogLeNet相比。我们还将该模型与现有的基于CNN的模型进行了比较，包括AlexNet，ResNet-50和MobileNet-V2。结果表明,该模型的平均推理时间减少了61.49%,41.78%和23.81%，分别。结果表明,所提出的增强模型在精度和计算效率方面都能显著提高性能,这有可能提高辣椒种植业的生产力。
