College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
Sensors (Basel). 2024 Aug 15;24(16):5279. doi: 10.3390/s24165279.
The number of maize leaves is an important indicator for assessing plant growth and regulating population structure. However, the traditional leaf counting method mainly relies on manual work, which is both time-consuming and straining, while the existing image processing methods have low accuracy and poor adaptability, making it difficult to meet the standards for practical application. To accurately detect the growth status of maize, an improved lightweight YOLOv8 maize leaf detection and counting method was proposed in this study. Firstly, the backbone of the YOLOv8 network is replaced using the StarNet network and the convolution and attention fusion module (CAFM) is introduced, which combines the local convolution and global attention mechanisms to enhance the ability of feature representation and fusion of information from different channels. Secondly, in the neck network part, the StarBlock module is used to improve the C2f module to capture more complex features while preserving the original feature information through jump connections to improve training stability and performance. Finally, a lightweight shared convolutional detection head (LSCD) is used to reduce repetitive computations and improve computational efficiency. The experimental results show that the precision, recall, and mAP50 of the improved model are 97.9%, 95.5%, and 97.5%, and the numbers of model parameters and model size are 1.8 M and 3.8 MB, which are reduced by 40.86% and 39.68% compared to YOLOv8. This study shows that the model improves the accuracy of maize leaf detection, assists breeders in making scientific decisions, provides a reference for the deployment and application of maize leaf number mobile end detection devices, and provides technical support for the high-quality assessment of maize growth.
玉米叶片数量是评估植物生长和调节群体结构的重要指标。然而，传统的叶片计数方法主要依赖于人工操作，既费时又费力，而现有的图像处理方法准确性低，适应性差，难以满足实际应用的标准。为了准确检测玉米的生长状况，本研究提出了一种改进的轻量级 YOLOv8 玉米叶片检测和计数方法。首先，用 StarNet 网络替换 YOLOv8 网络的骨干，引入卷积和注意力融合模块（CAFM），将局部卷积和全局注意力机制相结合，增强特征表示和融合不同通道信息的能力。其次，在颈部网络部分，使用 StarBlock 模块改进 C2f 模块，通过跳跃连接保留原始特征信息，同时捕捉更复杂的特征，提高训练稳定性和性能。最后，使用轻量级共享卷积检测头（LSCD）减少重复计算，提高计算效率。实验结果表明，改进后的模型的精度、召回率和 mAP50 分别为 97.9%、95.5%和 97.5%，模型参数数量和模型大小分别为 1.8 M 和 3.8 MB，与 YOLOv8 相比，分别减少了 40.86%和 39.68%。本研究表明，该模型提高了玉米叶片检测的准确性，有助于育种者做出科学决策，为玉米叶片数量移动终端检测设备的部署和应用提供了参考，为玉米生长的高质量评估提供了技术支持。
