Yao Qiong, Pan Pan, Zheng Xiaoming, Zhou Guomin, Zhang Jianhua
Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China.
College of Agriculture, Henan University, Kaifeng, China.
Front Plant Sci. 2025 Jun 2;16:1595386. doi: 10.3389/fpls.2025.1595386. eCollection 2025.
In response to the limited models for salt tolerance detection in wild rice, the subtle leaf features, and the difficulty in capturing salt stress characteristics, resulting in low recognition and detection rates and accuracy, a deep learning-based ST-YOLO wild rice seedling salt tolerance phenotype evaluation and identification model is proposed.
In order to improve accuracy and achieve model lightweighting, a multi branch structure DBB (Diverse Branch Block) is used to replace the convolutional layers in the C2f module, and a reparameterization module C2f DBB is proposed to replace some C2f modules. Diversified feature extraction paths are introduced to enhance the ability of feature extraction; Introducing CAFM (Context Aware Feature Modulation) convolution and attention fusion modules into the backbone network to enhance feature representation capabilities while improving the fusion of features at various scales; Design a more flexible and effective spatial pyramid pooling layer using deformable convolution and spatial information enhancement modules to improve the model's ability to represent target features and detection accuracy.
The experimental results show that the improved algorithm improves the average precision by 2.7% compared with the original network; the accuracy rate improves by 3.5%; and the recall rate improves by 4.9%.
The experimental results show that the improved model significantly improves in precision compared with the current mainstream model, and the model evaluates the salt tolerance level of wild rice varieties, and screens out a total of 2 varieties that are extremely salt tolerant and 7 varieties that are salt tolerant, which meets the real-time requirements, and has a certain reference value for the practical application.
针对野生稻耐盐性检测模型有限、叶片特征细微以及难以捕捉盐胁迫特征，导致识别和检测率及准确率较低的问题，提出了一种基于深度学习的ST - YOLO野生稻幼苗耐盐性表型评估与识别模型。
为提高准确率并实现模型轻量化，采用多分支结构DBB（多样分支块）替换C2f模块中的卷积层，提出重参数化模块C2f DBB以替换部分C2f模块。引入多样化特征提取路径以增强特征提取能力；在主干网络中引入CAFM（上下文感知特征调制）卷积和注意力融合模块以增强特征表示能力，同时改善不同尺度特征的融合；使用可变形卷积和空间信息增强模块设计更灵活有效的空间金字塔池化层，以提高模型表示目标特征的能力和检测准确率。
实验结果表明，改进算法与原网络相比，平均精度提高了2.7%；准确率提高了3.5%；召回率提高了4.9%。
实验结果表明，改进后的模型与当前主流模型相比，精度有显著提高，该模型评估了野生稻品种的耐盐性水平，共筛选出2个极耐盐品种和7个耐盐品种，满足实时性要求，对实际应用具有一定参考价值。
