Dai Weisi, Zhu Wenke, Zhou Guoxiong, Liu Genhua, Xu Jiaxin, Zhou Hongliang, Hu Yahui, Liu Zewei, Li Jinyang, Li Liujun
Faculty of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha, 410004 Hunan, China.
College of Bangor, Central South University of Forestry and Technology, Changsha, 410004 Hunan, China.
Plant Phenomics. 2024 Aug 5;6:0218. doi: 10.34133/plantphenomics.0218. eCollection 2024.
Rice leaf diseases have an important impact on modern farming, threatening crop health and yield. Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification. However, the diversity of rice growing environments and the complexity of leaf diseases pose challenges. To address these issues, this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer. First, it features the sparse global-update perceptron for real-time parameter updating, enhancing model stability and accuracy in learning irregular leaf features. Second, the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module (SRM) and channel reconstruction module (CRM), focusing on salient feature extraction and reducing background interference. Additionally, the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm, gradually reducing the stochastic search amplitude to minimize loss. This enhances the model's adaptability and robustness, particularly against fuzzy edge features. The experimental results show that AISOA-SSformer achieves an 83.1% MIoU, an 80.3% Dice coefficient, and a 76.5% recall on a homemade dataset, with a model size of only 14.71 million parameters. Compared with other popular algorithms, it demonstrates greater accuracy in rice leaf disease segmentation. This method effectively improves segmentation, providing valuable insights for modern plantation management. The data and code used in this study will be open sourced at https://github.com/ZhouGuoXiong/Rice-Leaf-Disease-Segmentation-Dataset-Code.
水稻叶部病害对现代农业产生重要影响，威胁着作物健康和产量。精确的语义分割技术对于分割患病叶片部分以及协助农民进行病害识别至关重要。然而，水稻生长环境的多样性和叶部病害的复杂性带来了挑战。为解决这些问题，本研究引入了一种基于Transformer架构的用于水稻叶部病虫害的创新语义分割算法AISOA-SSformer。首先，它具有用于实时参数更新的稀疏全局更新感知器，增强了模型在学习不规则叶片特征时的稳定性和准确性。其次，引入了显著特征注意力机制，使用空间重建模块（SRM）和通道重建模块（CRM）来分离和重组特征，专注于显著特征提取并减少背景干扰。此外，退火集成麻雀优化算法对麻雀算法进行微调，逐渐减小随机搜索幅度以最小化损失。这增强了模型的适应性和鲁棒性，特别是针对模糊边缘特征。实验结果表明，AISOA-SSformer在自制数据集上实现了83.1%的平均交并比、80.3%的Dice系数和76.5%的召回率，模型大小仅为1471万个参数。与其他流行算法相比，它在水稻叶部病害分割方面表现出更高的准确性。该方法有效地改进了分割，为现代种植管理提供了有价值的见解。本研究中使用的数据和代码将在https://github.com/ZhouGuoXiong/Rice-Leaf-Disease-Segmentation-Dataset-Code上开源。
