Jiang Dongshan, Liu Jinyang, Zhang Haomiao, Liang Wenxiang, Luo Ziqiu, An Wenlong, Li Shicong, Chen Xin, Yuan Xingxing, Gao Shangbing
Department of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China.
Jiangsu Academy of Agricultural Sciences, Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Institute of Industrial Crops, Nanjing, Jiangsu, China.
PLoS One. 2025 Jul 31;20(7):e0326328. doi: 10.1371/journal.pone.0326328. eCollection 2025.
Drought is one of the main factors affecting mung bean production in China. Screening drought-resistant germplasm resources and cultivating drought-resistant varieties are of great significance to the development of the mung bean industry in China. Combined with chlorophyll fluorescence imaging technology, this paper proposes a lightweight mung bean drought resistance identification network model based on YOLOv8, referred to as CSCA-YOLOv8. The model uses StarNet to replace the backbone network of YOLOv8 to reduce the size of the model. The C2f_Star module is introduced in the neck structure instead of the original C2f module. Then, in order to enhance the network's attention to the key regions in the feature map, the Context Anchor Attention Mechanism (CAA) module is also introduced into the fourth C2f_Star module. Then, a CGBD module is proposed in the neck structure to reconstruct the ordinary convolution to improve the feature extraction ability of the model for small targets. Finally, the SIoU loss function is used to replace CIoU to accelerate the convergence of the model. In the actual data analysis, we used the collected 4808 chlorophyll fluorescence images of the natural mung bean population under drought stress to make the Mungbean Drought Datatset(MDD) and made classification labels for each image according to different drought resistance levels, which were 0, 1, 2, 3, 4 and 5. We also verified the excellent performance and generalization performance of the model using the collected MDD dataset. The final experimental results show that compared with the YOLOv8s baseline model, the number of parameters of our proposed algorithm is reduced by 24%, the floating point number is reduced by 35%, and the accuracy is improved by 2.52%, which supports the deployment on embedded edge devices with limited computing power. Therefore, our proposed algorithm has great potential in the field of drought resistance identification and germplasm selection of mung bean.
干旱是影响我国绿豆生产的主要因素之一。筛选抗旱种质资源、培育抗旱品种对我国绿豆产业发展具有重要意义。本文结合叶绿素荧光成像技术，提出了一种基于YOLOv8的轻量级绿豆抗旱鉴定网络模型，简称CSCA - YOLOv8。该模型使用StarNet替换YOLOv8的主干网络以减小模型尺寸。在颈部结构中引入C2f_Star模块替代原来的C2f模块。然后，为增强网络对特征图中关键区域的关注，还在第四个C2f_Star模块中引入了上下文锚点注意力机制（CAA）模块。接着，在颈部结构中提出CGBD模块对普通卷积进行重构，以提高模型对小目标的特征提取能力。最后，使用SIoU损失函数替换CIoU以加速模型收敛。在实际数据分析中，我们利用收集到的4808张干旱胁迫下天然绿豆群体的叶绿素荧光图像制作了绿豆干旱数据集（MDD），并根据不同抗旱水平为每张图像制作分类标签，分别为0、1、2、3、4和5。我们还使用收集到的MDD数据集验证了该模型的优异性能和泛化性能。最终实验结果表明，与YOLOv8s基线模型相比，我们提出的算法参数数量减少了24%，浮点运算数减少了35%，准确率提高了2.52%，支持在计算能力有限的嵌入式边缘设备上进行部署。因此，我们提出的算法在绿豆抗旱鉴定和种质选择领域具有很大潜力。
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