Authors

Abstract

For achieving more efficient recognition results and deployment on mobile devices, a rice blast recognition model was constructed by making lightweight improvements to YOLOv5s. First, using YOLOv5s as the base, GhostConv was introduced to replace standard convolution in its backbone and neck, and LightC3 module was built to improve the C3 module in the neck. This significantly reduced the computational burden and model size. Furthermore, Concat operator was replaced with BiFPN and SE attention mechanism was integrated to maintain accuracy when reducing model complexity. These modifications enhanced the model's ability to capture fine-grained features and multi-scale information. The experimental results showed that the designed model had a 49% decrease in the number of model parameters and a 50% decrease in FLOPs without a decrease in precision on self-built rice blast dataset, compared with the YOLOv5s, achieving the good balance between detection performance and model lightweight. Then, an APP named RiceBlastDetector was built based on the model, achieving accurate detection in the scenario with the different characterization scale disease spots from experiments in the field, which can provide a reference for detecting other crop diseases.

Abstract in Chinese

为了在确保识别性能的前提下有效轻量化稻瘟病检测模型，本研究构建了一种轻量化的稻瘟病检测模型并部署于Android平台。首先，采用YOLOv5s作为基础框架。然后，引入GhostConv替代YOLOv5s主干和颈部中的标准卷积，并基于GhostConv构建了LightC3模块，以改进YOLOv5s颈部的C3模块，从而减少模型参数和复杂度。此外，为了在降低模型复杂度的同时保持检测精度，将YOLOv5s颈部中的Concat替换为BiFPN模块，以提升多尺度特征的融合效果，并将SE注意力机制集成到YOLOv5s主干网络中，以增强模型对稻瘟病重要特征的感知，从而提高稻瘟病识别的准确性。为了验证所提出模型的性能，我们通过自建的稻瘟病数据集进行了测试。实验结果表明，与YOLOv5s相比，所设计的模型在不降低精度的情况下，模型参数量减少了49%，FLOPs减少了50%，实现了检测性能与模型轻量化之间的良好平衡。随后，基于该模型开发了名为RiceBlastDetector的APP，并通过实地实验中不同特征尺度的病斑场景验证了其准确检测能力，能够为其他作物病害的检测提供参考。